Figures
STUDY AREA
ggplot() +
geom_sf(data=srme) +
geom_sf(data=wrnf, fill="darkgrey") +
coord_sf(crs="EPSG:32613") +
theme_bw(11)
Figure 1: Surface Reflectance / Spectral Response
head(ts)Tidy the time-series data.
- Remove cloud-contaminated pixels,
- Pivot the table to gather bands,
- Generate mean/median/stdev of reflectance by image date,
- Calculate weekly summaries
Filter using the pixel cloud probability, scene classification, and remove erroneously high reflectance values:
tsp <- ts %>%
filter(
probability < 51, # less than 51% cloud probability
! SCL > 7 & SCL <= 10, # remove points labeled as cloud
B8 < 5000 # remove points with very high near-infrared reflectance
) %>%
# add the month as a column
mutate(month = month(image_date, label=TRUE, abbr=TRUE))
tsp %>%
group_by(image_date) %>%
summarize(band = median(B8)) %>%
ggplot(aes(x=image_date,y=band)) +
geom_point(size=1.2) +
geom_smooth(method="gam") +
theme_bw(14)
ggplot(data=tsp, aes(x=B8, y=month)) +
geom_boxplot() +
coord_flip() +
theme_bw()
rm(ts)
Now pivot the table longer:
tsp <- tsp %>%
select(c(starts_with("B"),image_date,
SLAVI,NDRE,NDMI,ChlRE,IRECI)) %>%
group_by(image_date) %>%
summarize_all(list(median)) %>%
pivot_longer(
cols = -image_date,
names_to="band",
values_to="refl"
) %>%
# add a month and week label
mutate(
month = month(image_date, label=TRUE, abbr=TRUE),
week = isoweek(image_date),
week2 = cut.Date(image_date, breaks="2 week", labels=FALSE)
)
head(tsp)NAPlot of median daily observations:
# Reorder band factors
tsp$band <- factor(tsp$band,
levels = c("B2","B3","B4","B5","B6",
"B7","B8","B8A","B11","B12",
"ChlRE","IRECI","NDMI","NDRE","SLAVI"))
tsp %>%
filter(str_detect(band,"B")) %>%
ggplot(aes(x=image_date,y=refl,color=factor(band))) +
geom_line(linewidth=0.4) +
scale_color_viridis_d(option="turbo") +
labs(x="Acquisition Date",y="Reflectance",color="S2 MSI Band") +
theme_bw(14) +
theme(axis.title.y = element_text(size=11,face="italic",
margin = unit(c(0,0.4,0,0), "cm")),
axis.title.x = element_text(size=11,face="italic",
margin = unit(c(0.4,0,0,0), "cm")),
axis.text = element_text(size=10),
legend.text = element_text(size=10),
legend.title = element_text(size=11))
Figure 3: Spectral Response of Aspen Forest Presence Data
Gather the seasonal composite start and end dates:
# Summer
(start_summer <- week(as.Date("2019-06-01")))[1] 22(end_summer <- week(as.Date("2019-09-14")))[1] 37# Autumn
(start_autumn <- week(as.Date("2019-09-16")))[1] 37(end_autumn <- week(as.Date("2019-11-30")))[1] 48Generate the weekly median reflectance.
# List for band colors
band_colors <- c(
"B2" = "#1E90FF", # Blue: Dodger Blue
"B3" = "#00FF7F", # Green: Spring Green
"B4" = "#FF0000", # Red: Pure Red
"B5" = "#FFA07A", # Red Edge: Light Salmon
"B6" = "#FF8C00", # NIR: Dark Orange
"B7" = "#D2691E", # NIR: Chocolate
"B8" = "#FFD700", # NIR: Gold
"B8A" = "#B8860B", # NIR: Dark Goldenrod
"B11" = "#008B8B", # SWIR: Dark Cyan (dark shade of teal)
"B12" = "#800080" # SWIR: Purple
)
# Group by week, calculate the mean, plot
f3a <- tsp %>%
filter(str_detect(band,"B")) %>%
group_by(band,week) %>%
summarize(refl = mean(refl)) %>%
ggplot(aes(x=week,y=refl,color=factor(band))) +
geom_line(linewidth=0.6) +
scale_color_manual(values = band_colors) +
labs(x="Acquisition Week",y="Reflectance",color=NULL) +
theme_bw(11) +
theme(axis.title.y = element_text(size=11,face="italic",
margin = unit(c(0,0.4,0,0), "cm")),
axis.title.x = element_text(size=11,face="italic",
margin = unit(c(0.4,0,0,0), "cm")),
axis.text = element_text(size=10),
legend.text = element_text(size=8),
legend.title = element_text(size=8),
legend.position = c(0.2,0.85),
legend.direction = "horizontal",
legend.background = element_rect(fill="white", color="black"),
plot.margin = unit(c(1.2,0.5,1.2,0.5), 'lines'),
text = element_text(family = "Arial")) +
annotate('rect', xmin=start_summer, xmax=end_autumn, ymin=0, ymax=4000, alpha=.3, fill='gray') +
geom_vline(xintercept=start_autumn, linetype="dotted", lwd=0.8)
f3a
Figure 1B: Time-series charts of the spectral indices:
# All bands
tsp %>%
group_by(band,week) %>%
summarize(refl = mean(refl)) %>%
ggplot(aes(x=week, y=refl, group=1)) +
geom_line() +
annotate('rect', xmin=22, xmax=48, ymin=0, ymax=Inf, alpha=.3, fill='gray') +
geom_vline(xintercept=37, linetype="dotted") +
facet_wrap(. ~ band, scales = "free") +
theme_minimal() +
theme(axis.title.y = element_text(size=8,face="italic",
margin = unit(c(0,0.4,0,0), "cm")),
axis.title.x = element_text(size=8,face="italic",
margin = unit(c(0.4,0,0,0), "cm")),
axis.text = element_text(size=7),
strip.text.x = element_text(size = 8))
# Spectral indices
f3b <- tsp %>%
filter(!str_detect(band,"B")) %>%
group_by(band,week) %>%
summarize(refl = mean(refl)) %>%
ggplot(aes(x=week, y=refl, group=1)) +
geom_line(linewidth=0.6) +
labs(x="Acquisition Week",y="Reflectance") +
annotate('rect', xmin=start_summer, xmax=end_autumn, ymin=0, ymax=Inf, alpha=.3, fill='gray') +
geom_vline(xintercept=start_autumn, linetype="dotted", lwd=0.8) +
facet_wrap(. ~ band, scales = "free") +
theme_light(11) +
theme(axis.title.y = element_text(size=11,face="italic",
margin = unit(c(0,0.4,0,0), "cm")),
axis.title.x = element_text(size=11,face="italic",
margin = unit(c(0.4,0,0,0), "cm")),
axis.text = element_text(size=10),
strip.text.x = element_text(size = 11),
plot.margin = unit(c(1.2,0.5,1.2,0.5), 'lines'),
text = element_text(family = "Arial"))
f3b
Figure 3C: Boxplot of seasonal composites:
# Reorder band factors
tsp$band <- factor(tsp$band,
levels = c("B2","B3","B4","B5","B6",
"B7","B8","B8A","B11","B12",
"ChlRE","IRECI","NDMI","NDRE","SLAVI"))
cols <- c(
"Summer" = "#87CEEB",
"Autumn" = "#DAA520"
)
seasonal <- tsp %>%
mutate(season = if_else(week>=22&week<=37,"Summer","na"),
season = if_else(week>=37&week<=48,"Autumn",season)) %>%
filter(season!="na")
seasonal$season <- factor(seasonal$season, levels = c("Summer","Autumn"))
f3c <- seasonal %>%
filter(str_detect(band,"B")) %>%
ggplot(aes(x=band,y=refl,fill=season)) +
geom_boxplot(outlier.size = 0.5, outlier.color = "grey30") +
scale_fill_manual(values = cols) +
theme_bw(16) +
labs(x="Sentinel-2 MSI Band",y="Reflectance") +
theme(axis.title.y = element_text(size=11,face="italic",
margin = unit(c(0,0.4,0,0), "cm")),
axis.title.x = element_text(size=11,face="italic",
margin = unit(c(0.4,0,0,0), "cm")),
axis.text = element_text(size=10),
legend.position = "none",
plot.margin = unit(c(1.2,0.5,1.2,0.5), 'lines'),
text = element_text(family = "Arial"))
f3c
Figure 1D: Seasonal differences in the spectral indices
f3d <- seasonal %>%
filter(!str_detect(band,"B")) %>%
ggplot(aes(y=refl)) +
geom_boxplot(aes(fill=season)) +
scale_fill_manual(values = cols) +
facet_wrap(. ~ band, scales = "free") +
theme_bw(14) +
labs(fill="Season",y="Value") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size=11,face="italic",
margin = unit(c(0,0.4,0,0), "cm")),
axis.title.x = element_text(size=11,face="italic",
margin = unit(c(0.4,0,0,0), "cm")),
axis.text = element_text(size=10),
strip.text.x = element_text(size = 11),
legend.position = c(0.85,0.2),
legend.title = element_text(size=16),
legend.text = element_text(size=15),
plot.margin = unit(c(1.2,0.5,1.2,0.5), 'lines'),
text = element_text(family = "Arial"))
f3d
Combine the plots:
# Arrange in a multi-panel plot
f3 <- ggarrange(f3a,f3b,f3c,f3d, nrow=2,ncol=2,widths=c(1, 0.75), labels = c("A", "B", "C", "D"))Warning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in 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databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databasef3
# Save it out
ggsave(f3, file = "../../figures/Figure3_SpectralResponse.png",
dpi = 300, bg="white") # adjust dpi accordingly
Version 2:
f3_ <- ggarrange(f3a,ggarrange(f3c,f3d,nrow=1,ncol=2),
ncol=1,widths=c(1, 0.75),
labels = c("A", "B", "C"))Warning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font 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databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databasef3_
ggsave(f3_, file = "../../figures/Figure3_SpectralResponse_v2.png",
dpi = 300, bg="white") # adjust dpi accordinglySaving 14 x 8 in imageClean up the time-series / spectral response data:
rm(f3,f3_,f3a,f3b,f3c,f3d,seasonal,tsp)
gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 3486807 186.3 10554481 563.7 NA 10554481 563.7
Vcells 114778541 875.7 299947880 2288.5 16384 299930662 2288.3Figure 4: Model accuracy and feature importance plot
Best Performing Model:
Plot the AUC-PR and AUC-ROC.
glimpse(accmeas)Rows: 1,000
Columns: 18
$ model <dbl> 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 847, 84…
$ prSize <dbl> 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 2901, 290…
$ bgSize <dbl> 25590, 25590, 25590, 25590, 25590, 25590, 25590, 25590, 25590, 25590, 25590, 25590, 25590, 25590, 25590, 25590, 25590…
$ cutoff <dbl> 0.00000000, 0.01010101, 0.02020202, 0.03030303, 0.04040404, 0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.0909090…
$ tp <dbl> 2901, 2864, 2845, 2829, 2813, 2803, 2788, 2781, 2768, 2753, 2739, 2729, 2722, 2712, 2707, 2694, 2688, 2680, 2676, 267…
$ tpr <dbl> 1.0000000, 0.9872458, 0.9806963, 0.9751810, 0.9696656, 0.9662185, 0.9610479, 0.9586350, 0.9541537, 0.9489831, 0.94415…
$ fn <dbl> 0, 37, 56, 72, 88, 98, 113, 120, 133, 148, 162, 172, 179, 189, 194, 207, 213, 221, 225, 231, 240, 244, 248, 254, 265,…
$ tn <dbl> 0, 16107, 19280, 21100, 21893, 22463, 22844, 23177, 23424, 23628, 23829, 23975, 24089, 24194, 24305, 24399, 24499, 24…
$ tnr <dbl> 0.0000000, 0.6294256, 0.7534193, 0.8245408, 0.8555295, 0.8778038, 0.8926925, 0.9057054, 0.9153576, 0.9233294, 0.93118…
$ fp <dbl> 25590, 9483, 6310, 4490, 3697, 3127, 2746, 2413, 2166, 1962, 1761, 1615, 1501, 1396, 1285, 1191, 1091, 984, 902, 825,…
$ fpr <dbl> 1.000000000, 0.370574443, 0.246580696, 0.175459164, 0.144470496, 0.122196170, 0.107307542, 0.094294646, 0.084642438, …
$ specificity <dbl> 0.0000000, 0.6294256, 0.7534193, 0.8245408, 0.8555295, 0.8778038, 0.8926925, 0.9057054, 0.9153576, 0.9233294, 0.93118…
$ precision <dbl> 0.1018216, 0.2319592, 0.3107591, 0.3865282, 0.4321045, 0.4726813, 0.5037947, 0.5354255, 0.5610053, 0.5838812, 0.60866…
$ recall <dbl> 1.0000000, 0.9872458, 0.9806963, 0.9751810, 0.9696656, 0.9662185, 0.9610479, 0.9586350, 0.9541537, 0.9489831, 0.94415…
$ f1 <dbl> 0.1848242, 0.3756558, 0.4719642, 0.5536204, 0.5978111, 0.6348092, 0.6610551, 0.6870908, 0.7065731, 0.7229517, 0.74017…
$ accuracy <dbl> 0.1018216, 0.6658594, 0.7765610, 0.8398793, 0.8671510, 0.8868064, 0.8996525, 0.9110947, 0.9193079, 0.9259415, 0.93250…
$ gmean <dbl> 0.0000000, 0.7882878, 0.8595787, 0.8967031, 0.9108115, 0.9209508, 0.9262398, 0.9317944, 0.9345544, 0.9360684, 0.93764…
$ mcc <dbl> NA, 0.3763366, 0.4754020, 0.5535300, 0.5943626, 0.6287188, 0.6526159, 0.6770064, 0.6949038, 0.7098570, 0.7258652, 0.7…Calculate the model averages using the optimum cutoff value:
# Without taking the average
print("~~~~~Total (not averageing)~~~~~")[1] "~~~~~Total (not averageing)~~~~~"(cutoffOptF1 = accmeas[which.max(accmeas$f1),]$cutoff)[1] 0.6565657(precisionOptF1 = accmeas[which.max(accmeas$f1),]$precision)[1] 0.9575117(recallOptF1 = accmeas[which.max(accmeas$f1),]$recall)[1] 0.9131408(fprOpt = accmeas[which.max(accmeas$f1),]$fpr)[1] 0.01006562(tprOpt = accmeas[which.max(accmeas$f1),]$tpr)[1] 0.9131408# With the aggregation/averaging across models and threshold values
# Calculate the averages for each cutoff value across model runs
accmeas.mn <- accmeas %>%
mutate(cutoff = as.character(cutoff)) %>%
group_by(cutoff) %>%
summarise(
prMn = mean(prSize,na.rm=T), # size of presence data (mean)
prSd = sd(prSize,na.rm=T), # size of presence data (sd)
bgMn = mean(bgSize,na.rm=T), # size of background data (mean)
bgSd = sd(bgSize,na.rm=T), # size of background data (sd)
fprMn = mean(fpr,na.rm=T), # False Positive Rate (mean)
fprSd = sd(fpr,na.rm=T),
tprMn = mean(tpr,na.rm=T), # True Positive Rate (mean)
tprSd = sd(tpr,na.rm=T),
precision = mean(precision,na.rm=T), # precision (mean)
precisionSd = sd(precision,na.rm=T),
recall = mean(recall,na.rm=T), # recall (mean)
recallSd = sd(recall,na.rm=T),
f1 = mean(f1,na.rm=T), # F1 score (mean)
f1Sd = sd(f1,na.rm=T),
gmean = mean(gmean,na.rm=T), # Geometric Mean (mean)
gmeanSd = sd(gmean,na.rm=T),
mcc = mean(mcc,na.rm=T), # Matthew's Correlation Coefficient (mean)
mccSd = sd(mcc,na.rm=T),
accuracy = mean(accuracy,na.rm=T), # Overall accuracy (mean)
accuracySd = sd(accuracy,na.rm=T)
) %>%
ungroup() %>%
mutate(cutoff = round(as.double(cutoff),3))
# Calculate optimum threshold based on F1 statistic
cutoffOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$cutoff
precisionOptF1Mn = accmeas.mn[which.max(accmeas.mn$f1),]$precision
recallOptF1Mn = accmeas.mn[which.max(accmeas.mn$f1),]$recall
fprOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$fprMn
tprOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$tprMn
max_f1 <- max(accmeas.mn$f1, na.rm = TRUE)
# F1 based
print("F1-based accuracy metrics:")[1] "F1-based accuracy metrics:"paste0("F1 Score: ",(f1Opt = accmeas.mn[which.max(accmeas.mn$f1),]$f1))[1] "F1 Score: 0.910339477355629"paste0("Overall Accuracy: ",(oaOpt = accmeas.mn[which.max(accmeas.mn$f1),]$accuracy))[1] "Overall Accuracy: 0.972760973096824"paste0("Precision: ",(precOpt = accmeas.mn[which.max(accmeas.mn$f1),]$precision))[1] "Precision: 0.923031953935543"paste0("Recall: ",(recOpt = accmeas.mn[which.max(accmeas.mn$f1),]$recall))[1] "Recall: 0.899585913024552"paste0("Cutoff: ",(threshOpt = accmeas.mn[which.max(accmeas.mn$f1),]$cutoff))[1] "Cutoff: 0.424"# Mathews Correlation Coefficient
print("---------------------------")[1] "---------------------------"print("MCC-based accuracy metrics:")[1] "MCC-based accuracy metrics:"paste0("MCC: ",(mccOpt = accmeas.mn[which.max(accmeas.mn$mcc),]$mcc))[1] "MCC: 0.894765492997981"paste0("Overall Accuracy: ",(oaOptmcc = accmeas.mn[which.max(accmeas.mn$mcc),]$accuracy))[1] "Overall Accuracy: 0.97280285805149"paste0("Precision: ",(precOptmcc = accmeas.mn[which.max(accmeas.mn$mcc),]$precision))[1] "Precision: 0.925644674464425"paste0("Recall: ",(recOptmcc = accmeas.mn[which.max(accmeas.mn$mcc),]$recall))[1] "Recall: 0.89698605744059"paste0("Cutoff: ",(threshOptmcc = accmeas.mn[which.max(accmeas.mn$mcc),]$cutoff))[1] "Cutoff: 0.434"Plot the model accuracy results for the optimum threshold.
# Get the best F1 score for each model run
accmeas.best <- accmeas %>%
group_by(factor(model)) %>% top_n(1,f1) %>%
ungroup() %>%
mutate(model_iter = row_number())
print(paste0("Cutoff: Mean = ",round(mean(accmeas.best$cutoff),3),"; Standard Deviation: ",round(sd(accmeas.best$cutoff),3)))[1] "Cutoff: Mean = 0.441; Standard Deviation: 0.118"print(paste0("F1 Score: Mean = ",round(mean(accmeas.best$f1),3),"; Standard Deviation: ",round(sd(accmeas.best$f1),3)))[1] "F1 Score: Mean = 0.915; Standard Deviation: 0.016"Figure 4A: Model Accuracy (F1 and OA)
cols <- c("F1 Score"="#1f78b4","Overall Accuracy"="gray75")
f4a <- ggplot(data=accmeas.best) +
geom_hline(yintercept=0.972760973096824,linetype="dashed",color="gray85") +
geom_hline(yintercept=0.910339477355629,linetype="dashed",color="#1f78b4") +
geom_line(aes(x=factor(model_iter),y=accuracy,group=1),color="gray65") +
geom_line(aes(x=factor(model_iter),y=f1,group=1),color="#1f78b4") +
geom_point(aes(x=factor(model_iter),y=f1,size=f1), color="#1f78b4") +
geom_point(aes(x=factor(model_iter),y=accuracy,size=accuracy), color="gray65") +
scale_size(range = c(3,6)) +
coord_cartesian(ylim = c(0.85, 1)) +
labs(x="Model Iteration", y="Score", tag="A") +
annotate("text", x = 3, y = 0.99, label = paste("Average Overall Accuracy: ", round(oaOpt, 2))) +
annotate("text", x = 2.6, y = 0.88, label = paste("Average F1 Score: ", round(f1Opt, 2))) +
theme_bw() +
guides(size="none") +
theme(
plot.margin = unit(c(0.2,0.2,0.2,0.2), 'lines'),
axis.title.y = element_text(size=11,face="italic",
margin = unit(c(0,0.4,0,0), "cm")),
axis.title.x = element_text(size=11,face="italic",
margin = unit(c(0.4,0,0,0), "cm")),
axis.text = element_text(size=10),
strip.text.x = element_text(size = 11),
text = element_text(family = "Arial"))
f4a
ggsave(f4a, file = "../../figures/Figure4A_Best_Model_Acc_F1.png",
dpi = 300, bg="white") # adjust dpi accordingly
Figure 4B: Sensitivity Analysis
# Labels for each model subset
labels_ = c(
"winter_sar" = "M1",
"summer_sar" = "M2",
"summer_winter_sar_text" = "M3",
"summer_winter_sar" = "M4",
"autumn_spectral" = "M5",
"summer_spectral" = "M6",
"summer_spectral_ind" = "M7",
"spectral_sar_ind" = "M8",
"summer_autumn_spectral" = "M9",
"spectral_sar" = "M10",
"final" = "Final Model"
)
# Add in the final model to the sensitivity analysis
add <- accmeas.best %>%
ungroup() %>%
select(model,f1) %>%
mutate(subset = "final") %>%
rename(f1Max = f1)
glimpse(add)Rows: 10
Columns: 3
$ model <dbl> 847, 991, 343, 197, 783, 9, 283, 777, 588, 708
$ f1Max <dbl> 0.8906140, 0.9021872, 0.9219364, 0.9084126, 0.9225152, 0.9348000, 0.8911798, 0.9283032, 0.9264555, 0.9200240
$ subset <chr> "final", "final", "final", "final", "final", "final", "final", "final", "final", "final"accmeas.s <- accmeas.s %>%
bind_rows(add)
rm(add)
print(unique(accmeas.s$subset)) [1] "autumn_spectral" "spectral_sar_ind" "spectral_sar" "summer_autumn_spectral" "summer_sar"
[6] "summer_spectral_ind" "summer_spectral" "summer_winter_sar_text" "summer_winter_sar" "winter_sar"
[11] "final" # Generate the box plots of F1 score
f4b <- ggplot(data=accmeas.s, aes(x=reorder(subset,f1Max),y=f1Max)) +
geom_boxplot() +
scale_x_discrete(labels=labels_) +
labs(x="Input Features",y="Maximum F1 Score", tag="B") +
theme_bw(11) +
theme(
plot.margin = unit(c(0.2,0.2,0.2,0.2), 'lines'),
axis.title.y = element_text(size=11,face="italic",
margin = unit(c(0,0.4,0,0), "cm")),
axis.title.x = element_text(size=11,face="italic",
margin = unit(c(0.4,0,0,0), "cm")),
axis.text = element_text(size=10),
strip.text.x = element_text(size = 11),
axis.text.x = element_text(angle = 45, hjust=1),
text = element_text(family = "Arial"))
f4b
# Save out
ggsave(f4b, file = "../../figures/Figure4B_Model_Sensitivity.png",
dpi = 300, bg="white") # adjust dpi accordinglySaving 7.29 x 4.51 in image
Combine the two figures for Figure 4AB:
f4 <- ggarrange(f4a,f4b,nrow=2)Warning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databasef4
ggsave(f4, file = "../../figures/Figure4AB_Model_Accuracy_Sensitivity.png",
width=7.5, height=7.5, dpi = 300, bg="white") # adjust dpi accordingly
Figure 6: Feature importance from the best model
glimpse(ftr_imp)Rows: 10
Columns: 40
$ `system:index` <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
$ B11_autumn <dbl> 21.63001, 24.71046, 26.31618, 24.16826, 28.99346, 19.68919, 22.29900, 25.98756, 23.72236, 18.54693
$ B11_summer <dbl> 32.61777, 88.15568, 60.08632, 53.96426, 73.95518, 72.80209, 52.43871, 80.31342, 50.76397, 69.10347
$ B12_autumn <dbl> 8.754374, 7.762443, 8.792581, 9.551003, 12.785215, 5.251996, 8.192395, 8.501508, 10.572703, 6.456701
$ B12_summer <dbl> 31.83856, 52.96160, 38.76007, 38.55945, 50.41913, 48.07633, 39.51833, 42.83267, 29.81382, 40.09193
$ B2_autumn <dbl> 11.01432, 37.32868, 23.77948, 17.83080, 33.41181, 19.94616, 16.71003, 24.59457, 19.34141, 19.19359
$ B2_summer <dbl> 1.544011, 4.428377, 2.925933, 3.246131, 3.434643, 3.485534, 2.789854, 3.784211, 4.843380, 3.900492
$ B3_autumn <dbl> 32.90573, 92.48967, 84.06609, 65.37862, 69.08507, 72.15569, 53.37248, 90.80987, 69.53004, 63.90807
$ B3_summer <dbl> 14.483419, 16.379753, 13.108126, 9.564537, 10.055796, 12.936688, 10.481147, 14.919178, 18.070886, 15.289636
$ B4_autumn <dbl> 15.46758, 31.21614, 26.98158, 17.52866, 23.64321, 24.35671, 21.20621, 25.54006, 25.99864, 18.95956
$ B4_summer <dbl> 25.03937, 34.02723, 31.25796, 19.47697, 20.00762, 34.28381, 25.24520, 31.07726, 32.81473, 34.92248
$ B5_autumn <dbl> 7.159686, 22.725072, 20.973219, 13.242495, 16.899435, 16.774081, 12.558062, 21.655292, 24.347260, 13.619232
$ B5_summer <dbl> 13.05487, 23.06441, 21.29735, 15.21438, 10.23691, 25.19220, 15.07107, 26.41282, 21.99278, 16.44929
$ B6_autumn <dbl> 3.143607, 12.979872, 16.181342, 9.231907, 9.704828, 13.313262, 6.281355, 12.702162, 13.859885, 14.459542
$ B6_summer <dbl> 9.630015, 14.326921, 16.189771, 8.770566, 9.178170, 9.114085, 7.018957, 18.397494, 13.348548, 14.043514
$ B8_autumn <dbl> 1.751561, 8.387896, 7.606446, 5.830232, 4.035430, 5.854015, 2.170036, 5.069091, 8.652400, 3.776282
$ ChlRE_autumn <dbl> 12.98923, 21.18098, 22.66903, 15.78830, 17.69833, 17.75489, 11.87762, 20.72763, 23.82522, 20.97342
$ ChlRE_summer <dbl> 82.17996, 101.93907, 105.73092, 103.51654, 102.97492, 105.56823, 98.30599, 101.28398, 96.24895, 110.02008
$ IRECI_autumn <dbl> 1.6542028, 3.2196484, 2.3650415, 0.7022645, 1.6526279, 1.0071467, 1.0040322, 3.2029031, 2.5123898, 1.0397889
$ IRECI_summer <dbl> 14.16647, 21.94451, 24.55819, 21.98607, 22.44355, 20.32083, 17.31854, 21.83391, 26.52660, 18.86483
$ NDMI_autumn <dbl> 5.284292, 6.409813, 8.680610, 9.148087, 8.801485, 5.467554, 4.800973, 7.988817, 12.338028, 7.960924
$ NDMI_summer <dbl> 26.60976, 25.41538, 25.41944, 38.26568, 42.14366, 35.12636, 32.01464, 26.38395, 17.69483, 31.65604
$ NDRE_autumn <dbl> 7.091025, 13.491095, 9.905432, 5.922877, 8.751692, 8.572938, 7.119315, 12.678073, 9.988859, 9.150386
$ NDRE_summer <dbl> 74.55249, 85.95997, 92.60474, 89.07581, 94.71430, 93.68764, 83.93495, 89.60477, 93.61925, 87.67304
$ SLAVI_autumn <dbl> 0.7581965, 2.1182702, 1.5958263, 0.6020280, 1.7417527, 1.2952044, 1.5542184, 1.7272081, 0.9213949, 0.9790948
$ SLAVI_summer <dbl> 44.26112, 54.30483, 56.35111, 52.61809, 52.49489, 51.57909, 44.03826, 52.53019, 45.75043, 49.18132
$ VH_summer <dbl> 148.4425, 178.3843, 171.8808, 146.9129, 168.2566, 144.1681, 165.2832, 160.8539, 177.6198, 149.5917
$ VH_summer_contrast <dbl> 7.431997, 3.403814, 2.495050, 2.771170, 5.822993, 4.309862, 2.731022, 3.218622, 5.673280, 3.403127
$ VH_summer_ent <dbl> 8.109652, 4.510600, 6.983570, 3.173383, 5.992742, 4.668601, 5.488646, 7.298885, 6.797872, 3.575809
$ VH_winter <dbl> 32.73627, 39.91918, 38.24390, 32.68237, 43.17811, 36.18398, 42.17934, 39.50431, 47.89564, 42.82106
$ VH_winter_contrast <dbl> 7.067432, 4.903738, 5.184350, 3.717707, 5.418322, 7.437393, 4.987159, 4.303950, 5.951391, 2.591191
$ VH_winter_ent <dbl> 3.904413, 3.505374, 3.297951, 4.277354, 3.182406, 5.360329, 3.818240, 3.681942, 7.229823, 2.231365
$ VV_summer <dbl> 82.46562, 87.00165, 80.80692, 71.20855, 76.14901, 80.24709, 86.91908, 82.11989, 76.76129, 69.76924
$ VV_summer_contrast <dbl> 4.015073, 4.000985, 6.338935, 4.541748, 5.589750, 6.125613, 4.943460, 3.774304, 5.980394, 5.599165
$ VV_summer_ent <dbl> 3.709408, 3.447794, 6.666000, 3.654588, 5.257164, 9.238666, 4.542707, 4.675496, 7.836229, 6.438189
$ VV_winter <dbl> 19.56131, 22.65798, 22.86519, 19.75495, 19.27481, 25.02942, 24.92029, 20.98694, 24.95799, 22.82763
$ VV_winter_contrast <dbl> 0.9623009, 2.4274726, 3.7450767, 1.6043663, 2.6151410, 1.8423834, 1.8399639, 1.1207479, 4.6077801, 1.4807937
$ VV_winter_ent <dbl> 0.7249480, 1.5235996, 1.5404339, 0.2233372, 1.7780329, 0.8068386, 1.2452230, 0.5372814, 2.7943568, 1.9884459
$ seed <dbl> 923, 940, 534, 301, 823, 754, 610, 358, 201, 811
$ .geo <chr> "{\"type\":\"MultiPoint\",\"coordinates\":[]}", "{\"type\":\"MultiPoint\",\"coordinates\":[]}", "{\"type\":\"M…
# # Tidy the data frame
# df.imp <- ftr_imp %>%
# select(-c(.geo,`system:index`)) %>%
# rename(model = seed) %>%
# mutate(model = as.factor(model)) %>%
# # Summarize across to grab the relative importance
# rowwise() %>%
# mutate(total = sum(c_across(B11_autumn:VV_winter_ent))) %>%
# ungroup() %>%
# mutate(across(B11_autumn:VV_winter_ent, ~ . * 100 / total)) %>%
# select(-total)
# glimpse(df.imp)
# Tidy the data frame
df.imp <- ftr_imp %>%
select(-c(.geo,`system:index`)) %>%
rename(model = seed) %>%
mutate(model = as.factor(model)) %>%
# Summarize across to grab the relative importance
group_by(model) %>%
mutate(total = sum(across(B11_autumn:VV_winter_ent))) %>%
mutate(across(B11_autumn:VV_winter_ent, ~ . / total)) %>%
ungroup() %>%
select(-total)
# Pivot longer
df.imp.p <- df.imp %>%
pivot_longer(cols = -model) %>%
rename(importance = value,
band = name) %>%
mutate(season = if_else(str_detect(band,"_autumn"), "Autumn Spectral", "Summer Spectral"),
season = if_else(str_detect(band,"VV_winter"), "Winter SAR", season),
season = if_else(str_detect(band,"VH_winter"), "Winter SAR", season),
season = if_else(str_detect(band,"VV_summer"), "Summer SAR", season),
season = if_else(str_detect(band,"VH_summer"), "Summer SAR", season))
glimpse(df.imp.p)Rows: 370
Columns: 4
$ model <fct> 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923, 923…
$ band <chr> "B11_autumn", "B11_summer", "B12_autumn", "B12_summer", "B2_autumn", "B2_summer", "B3_autumn", "B3_summer", "B4_autumn…
$ importance <dbl> 0.0264195433, 0.0398403235, 0.0106928546, 0.0388885671, 0.0134532179, 0.0018859015, 0.0401920479, 0.0176904816, 0.0188…
$ season <chr> "Autumn Spectral", "Summer Spectral", "Autumn Spectral", "Summer Spectral", "Autumn Spectral", "Summer Spectral", "Aut…
# Grab the top 20 over all model runs
top <- df.imp.p %>%
group_by(band) %>%
summarize(median = median(importance)) %>%
ungroup()
top <- head(arrange(top,desc(median)), n = 20)
# Color palette
cols <- c(
"Summer Spectral" = "#87CEEB",
"Autumn Spectral" = "#DAA520",
"Winter SAR" = "gray29",
"Summer SAR" = "gray89"
)
# Boxplot
# %>% filter(band %in% top$band)
f6 <- ggplot(data=df.imp.p %>% filter(band %in% top$band),
aes(x=reorder(band,importance), fill=season)) +
geom_boxplot(aes(y=importance), position = position_dodge(0.5)) +
scale_fill_manual(values = cols,
labels = c("Autumn Spectral","Summer SAR","Summer Spectral","Winter SAR")) +
coord_flip() +
theme_bw(11) +
theme(axis.text.x = element_text(size=11),
axis.text.y = element_text(angle = 0, vjust=0, size=11),
axis.title = element_text(size=11,face="italic"),
legend.position = "bottom",
legend.justification = c(1.2,0.5), # Left-aligns the legends
legend.text = element_text(size=11),
text = element_text(family = "Arial"),
plot.margin=unit(c(0.5,0.5,0.5,0.5),"cm")) +
labs(x="Feature", y="Importance", fill="Season: ")
f6
ggsave(f6, file = "../../figures/Figure6_FeatureImportance.png",
dpi=300, bg="white") # adjust dpi accordingly
Clean up!
rm(list = ls.str(mode = 'numeric'))
rm(accmeas,accmeas.best,accmeas.mn,accmeas.s,df.imp,
df.imp.p,f4,f4a,f4b,f6,ftr_imp,top)
gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 3476244 185.7 10554481 563.7 NA 10554481 563.7
Vcells 114720624 875.3 299947880 2288.5 16384 299930662 2288.3Figures 7, 8 - Case Study: Landscape Patch Dynamics
Figure 7: Spatial Agreement
brewer.pal(n=5,"Set1")[1] "#E41A1C" "#377EB8" "#4DAF4A" "#984EA3" "#FF7F00"cols <- c("#4DAF4A", "#984EA3", "#FF7F00")
glimpse(ref.global)Rows: 30
Columns: 9
$ blocksize <dbl> 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9
$ tp <dbl> 9624656, 2302057, 1183801, 756552, 537725, 9054690, 2366565, 1233728, 792221, 563383, 11730465, 2448883, 1170711, 71896…
$ fp <dbl> 7010703, 1121162, 483983, 278722, 184080, 7580669, 1056654, 434056, 243053, 158422, 4904894, 974336, 497073, 316306, 22…
$ fn <dbl> 5626219, 1153016, 524296, 301318, 194575, 7147207, 1796140, 831246, 471351, 298504, 15402301, 1788412, 602423, 289372, …
$ prec <dbl> 0.5785662, 0.6724831, 0.7098047, 0.7307747, 0.7449727, 0.5443039, 0.6913274, 0.7397409, 0.7652283, 0.7805197, 0.7051525…
$ rec <dbl> 0.6310888, 0.6662832, 0.6930526, 0.7151654, 0.7342961, 0.5588660, 0.5685161, 0.5974545, 0.6269694, 0.6536623, 0.4323358…
$ source <chr> "LFEVT", "LFEVT", "LFEVT", "LFEVT", "LFEVT", "TreeMap", "TreeMap", "TreeMap", "TreeMap", "TreeMap", "ITSP", "ITSP", "IT…
$ region <chr> "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME",…
$ f1 <dbl> 0.6036872, 0.6693688, 0.7013286, 0.7228858, 0.7395958, 0.5514888, 0.6239359, 0.6610276, 0.6892336, 0.7114805, 0.5360278…# Get an average f1 score table
f1mn <- ref.global %>%
group_by
# Add a statistics column for legend (could do this for F1 too ...)
ref.global.m <- reshape2::melt(
ref.global, id.vars = c("blocksize", "source", "region"),
measure.vars = c("prec", "rec"), variable.name = "statistic"
)
glimpse(ref.global.m)Rows: 60
Columns: 5
$ blocksize <dbl> 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9,…
$ source <chr> "LFEVT", "LFEVT", "LFEVT", "LFEVT", "LFEVT", "TreeMap", "TreeMap", "TreeMap", "TreeMap", "TreeMap", "ITSP", "ITSP", "IT…
$ region <chr> "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME", "SRME",…
$ statistic <fct> prec, prec, prec, prec, prec, prec, prec, prec, prec, prec, prec, prec, prec, prec, prec, prec, prec, prec, prec, prec,…
$ value <dbl> 0.5785662, 0.6724831, 0.7098047, 0.7307747, 0.7449727, 0.5443039, 0.6913274, 0.7397409, 0.7652283, 0.7805197, 0.7051525…f7a <- ggplot(data=ref.global.m) +
geom_line(aes(x=blocksize,y=value,color=factor(source), linetype=statistic), linewidth=0.6) +
geom_point(data=ref.global, aes(x=blocksize, y=f1, color=source)) +
scale_x_continuous(breaks=c(1,3,5,7,9)) +
scale_linetype_manual(values = c("prec" = "dotted", "rec" = "solid"), name="Statistic: ",
labels=c("Precision","Recall")) +
facet_wrap(~region, scales = "fixed") +
scale_color_manual(values=cols, labels=c("LANDFIRE EVT","USFS TreeMap","USFS ITSP"), name="Source: ") +
theme_light(14) +
ylim(0,1) +
theme(legend.position = "top",
legend.box = "vertical",
legend.justification = c(0.5,0.5), # Left-aligns the legends
legend.spacing.y = unit(-10, "pt"), # Adjusts the gap between the legends
legend.text = ggtext::element_markdown(halign = 0), # This aligns each individual legend's text to the left
axis.title = element_text(size=12,face="italic"),
text = element_text(family = "Arial")) +
labs(x="Blocksize (pixels)",y="Value")
f7a
ggsave(f7a, file = "../../figures/Figure7_Global_PrecRec_Agreement.png", dpi=300)Saving 7.29 x 4.51 in image
Focal accuracy:
# ref.focal$refbudens <- log(1 + ref.focal$tp + ref.focal$fn)
#
# ggplot(ref.focal %>% filter(region=="SRME"), aes(x = prec, y = rec, color = refbudens)) +
# geom_point(size = 2, alpha = 0.9) +
# scale_color_viridis_c() +
# facet_grid(rows = vars(geog_scale), cols = vars(blocksize), scales = "fixed") +
# theme_light() +
# labs(
# title = 'Spatially explicit accuracy: Precision (x) vs. Recall (y)',
# subtitle = 'for multiple analytical units and spatial support levels',
# color = 'refbudens',
# x = 'Precision',
# y = 'Recall'
# ) +
# coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) +
# theme(legend.position = "bottom")
Join the two plots:
# f7 <- ggarrange(global_precrec,focal.prec,focal.rec,nrow=1,labels=c("A","B","C"))
# f7
# ggsave(f7, file="../../figures/Figure7_Global_Focal_Agreement.png", dpi=300)Clean up!
rm(ref.global,ref.global.m,f7a,f7b,f7,ref.focal)Warning: object 'f7b' not foundWarning: object 'f7' not foundgc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 3500531 187.0 10554481 563.7 NA 10554481 563.7
Vcells 42536015 324.6 239958304 1830.8 16384 299930662 2288.3Figure 8: Landscape Patch Dynamics
unique(factor(patch_metrics$source))[1] LFEVT ITSP TreeMap Aspen10m Aspen30m
Levels: Aspen10m Aspen30m ITSP LFEVT TreeMapsummary(patch_metrics) X index area perimeter perimeter_area_ratio shape_index region
Min. : 0 Min. : 0 Min. : 0.01 Min. : 40 Min. : 72.95 Min. : 1.000 Length:5145088
1st Qu.: 180181 1st Qu.: 180181 1st Qu.: 0.05 1st Qu.: 100 1st Qu.:1000.00 1st Qu.: 1.000 Class :character
Median : 489868 Median : 489868 Median : 0.09 Median : 120 Median :1333.33 Median : 1.000 Mode :character
Mean : 594419 Mean : 594419 Mean : 1.72 Mean : 561 Mean :1728.38 Mean : 1.249
3rd Qu.: 920401 3rd Qu.: 920401 3rd Qu.: 0.27 3rd Qu.: 280 3rd Qu.:2333.33 3rd Qu.: 1.333
Max. :1822292 Max. :1822292 Max. :145661.31 Max. :16467240 Max. :4000.00 Max. :119.887
source
Length:5145088
Class :character
Mode :character
Calculate some grouped statistics (to compare with the class metrics results)
# Also create a grouped summary
(patch.summary <- patch_metrics %>%
group_by(source,region) %>%
summarize(area_md = median(area), # convert to hectares
area_mn = mean(area),
area_sum = sum(area),
perim_md = median(perimeter),
perim_mn = mean(perimeter),
par_md = median(perimeter_area_ratio),
par_mn = mean(perimeter_area_ratio),
si_md = median(shape_index),
si_mn = mean(shape_index)) %>%
ungroup())
(patch.long <- patch.summary %>%
pivot_longer(cols = c(contains("_")),
names_to = "metric",
values_to = "value"))
patch.long$source <- factor(patch.long$source,levels=c('Aspen10m', 'Aspen30m', 'LFEVT', 'TreeMap', 'ITSP'))
(ggplot(data = patch.long, aes(x=source, y=value, fill=region)) +
geom_bar(stat="identity", position="dodge", width=0.7) +
facet_wrap(~metric, scales="free") +
theme_minimal(14) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
text = element_text(family = "Arial")))
(f8s <- ggplot(data = patch.long, aes(x=source, y=value, fill=region)) +
geom_bar(stat="identity", position="dodge", width=0.7) +
theme_minimal(14) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
text = element_text(family = "Arial")))
Grab some statistics on the patch size:
(a <- patch.long %>% filter(metric=="area_mn",source=="Aspen10m"))(b <- patch.long %>% filter(metric=="area_mn",source=="LFEVT"))
(c <- patch.long %>% filter(metric=="area_md",source=="Aspen10m"))(d <- patch.long %>% filter(metric=="area_md",source=="LFEVT"))
print("Mean and median patch sizes: ")[1] "Mean and median patch sizes: "paste0("Aspen10m mean: ",a$value)[1] "Aspen10m mean: 0.520332932190088" "Aspen10m mean: 0.728109887310855"paste0("LFEVT mean: ",b$value)[1] "LFEVT mean: 1.77358052624186" "LFEVT mean: 3.39937585008331"paste0("Aspen10m median: ",c$value)[1] "Aspen10m median: 0.03" "Aspen10m median: 0.02"paste0("LFEVT mean: ",d$value)[1] "LFEVT mean: 0.18" "LFEVT mean: 0.18"print("Difference in mean area: ")[1] "Difference in mean area: "((b$value-a$value)/b$value)*100[1] 70.66201 78.58107print("Difference in median area: ")[1] "Difference in median area: "((d$value-c$value)/d$value)*100[1] 83.33333 88.88889print("Mean Perimeter area ratio: ")[1] "Mean Perimeter area ratio: "(a <- patch.long %>% filter(metric=="par_mn",source=="Aspen10m"))(b <- patch.long %>% filter(metric=="par_mn",source=="LFEVT"))
print("Difference in PAR: ")[1] "Difference in PAR: "((b$value-a$value)/b$value)*100[1] -154.6593 -169.6188rm(a,b,c,d)Boxplot as facet wrap for patch metrics:
brewer.pal(n=5,"Set1")[1] "#E41A1C" "#377EB8" "#4DAF4A" "#984EA3" "#FF7F00"cols <- c("#005a32","#a1d99b")
(df <- patch_metrics %>%
# mutate(area_ha = area*100) %>%
select(-c(X,index,shape_index)) %>%
pivot_longer(contains(c("area","perimeter_area_ratio"))) %>%
arrange(source, desc(value)) %>%
mutate(name = as.character(name),
name = recode(name,
"area" = "Patch Size (ha)",
"perimeter_area_ratio" = "Perimeter/Area Ratio")))
df$source <- factor(df$source,
levels=c('Aspen10m', 'Aspen30m', 'LFEVT', 'TreeMap', 'ITSP'))
(f8a <- ggplot(data=df, aes(y = value, x = factor(source), fill = factor(region))) +
geom_boxplot(outlier.size = 0.2) +
scale_y_continuous(trans="log10",labels=label_number_si(accuracy = 1)) +
scale_fill_manual(values=cols) +
facet_wrap(~name, scales = "free_y", nrow=1) +
labs(x="\nData Source",y="Value",title="Patch Metrics", fill="Region: ") +
theme_light(12) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size=11),
axis.text.y = element_text(angle = 0, vjust=0.1, size=11),
axis.title.y = element_text(size=12, face="italic", vjust=1),
axis.title.x = element_text(size=12, face="italic", vjust=-1),
plot.margin = unit(c(0.5,0.5,0.5,0.5),"cm"),
legend.position="top",
legend.spacing.x = unit(0.5,"cm"),
text = element_text(family = "Arial")))Warning: `label_number_si()` was deprecated in scales 1.2.0.
Please use the `scale_cut` argument of `label_number()` instead.
ggsave(f8a, file = "../../figures/Figure8A_patch_metrics.png", dpi = 300) # adjust dpi accordinglySaving 10 x 5 in imagePlot class metrics as facet wrap:
# Calculate area in square meters
srme_area_km2 <- st_area(srme) / 1e6
wrnf_area_km2 <- st_area(wrnf) / 1e6
(df <- class_metrics %>%
mutate(total_area = total_area*0.01,
prop_area = if_else(region=="SRME", as.double((total_area/srme_area_km2*100)), 0.0),
prop_area = if_else(region=="WRNF", as.double((total_area/wrnf_area_km2*100)), prop_area)) %>%
select(-c(X)) %>%
pivot_longer(cols = c(contains("_")),
names_to = "metric",
values_to = "value") %>%
mutate(metric = as.character(metric),
metric = recode(metric,
"n_patch" = "Number of Patches",
"patch_den" = "Patch Density",
"total_area" = "Total area (km2)",
"prop_area" = "Proportion of Area")))
df$source <- factor(df$source, levels=c('Aspen10m', 'Aspen30m', 'LFEVT', 'TreeMap', 'ITSP'))
head(df)
(f8b <- ggplot(data=df, aes(y=value, x=factor(source), fill=factor(region))) +
geom_bar(stat="identity", position="dodge", width=0.7) +
scale_y_continuous(labels=scales::label_number_si(accuracy = 1)) +
scale_fill_manual(values=cols) +
labs(x="", y="Value", title="Landscape Metrics", fill="Region: ") +
theme(axis.title.x = NULL) +
facet_wrap(~metric, scales = "free_y") +
theme_light(12) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size=11),
axis.text.y = element_text(angle = 0, vjust=0.1, size=11),
axis.title.y = element_text(size=12, face="italic", vjust=0.5),
axis.title.x = element_text(size=12, face="italic", vjust=-0.5),
plot.margin = unit(c(0.5,0.5,0.5,0.5),"cm"),
legend.position = "top",
legend.spacing.x = unit(0.5,"cm"),
text = element_text(family = "Arial")))
ggsave(f8b, file = "../../figures/Figure8B_landscape_metrics.png", dpi = 300) # adjust dpi accordingly
(arr <- ggarrange(f8b,f8a,nrow=2,ncol=1,labels=c("A","B"), common.legend = T))Warning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font databaseWarning: font family 'Arial' not found in PostScript font 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ggsave(arr, file="../../figures/Figure8_Landscape_Patch_Metrics.png", dpi=300)Saving 10 x 10 in imageThe 10-m aspen classification identified Xx more patch than reference images:
print("% Difference in number of patches: ")[1] "% Difference in number of patches: "a <- df %>% filter(metric=="Number of Patches",source=="Aspen10m")
b <- df %>% filter(metric=="Number of Patches",source=="LFEVT")
((b$value-a$value)/b$value)*100[1] -135.4679 -282.3586print("% Difference in patch density: ")[1] "% Difference in patch density: "a <- df %>% filter(metric=="Patch Density",source=="Aspen10m")
b <- df %>% filter(metric=="Patch Density",source=="LFEVT")
((b$value-a$value)/b$value)*100[1] -240.8549 -366.8768print("% Difference in total area:")[1] "% Difference in total area:"a <- df %>% filter(metric=="Total area (km2)",source=="Aspen10m")
aa <- df %>% filter(metric=="Total area (km2)",source=="Aspen30m")
b <- df %>% filter(metric=="Total area (km2)",source=="LFEVT")
((b$value-a$value)/b$value)*100[1] 30.91845 18.10289((b$value-aa$value)/b$value)*100[1] -9.07802 -20.41752rm(a,aa,b)
Supplemental
Figure S1: Phenology across the SRME study region
# Spatial map(s) of key phenological metrics
breaks_dates <- as.Date(c("2020-01-01", "2021-01-01", "2022-01-01"))
breaks_numeric <- as.numeric(breaks_dates) # Convert to numeric (days since 1970-01-01, by default)
labels_dates <- format(breaks_dates, "%Y-%m-%d")
p1 <- ggplot(data=blocks) +
geom_sf(aes(fill=Maturity_1)) +
scale_fill_continuous(low = "lightgreen", high = "darkgreen",
labels = function(x) format(as.Date(as.numeric(x), origin="1970-01-01"), "%B %d")) +
geom_sf(data=srme,fill=NA,color="black",size=2.5) +
labs(fill="Maturity") +
guides(fill = guide_colourbar(barwidth = 0.5, barheight = 5.5, ticks=F,
label.position = "right", title.position = "top",
label.theme = element_text(angle = 0, size = 8))) +
theme_void()
p2 <- ggplot(data=blocks) +
geom_sf(aes(fill=Dormancy_1)) +
scale_fill_continuous(low = "#ffffd4", high = "#993404",
labels = function(x) format(as.Date(as.numeric(x), origin="1970-01-01"), "%B %d")) +
geom_sf(data=srme,fill=NA,color="black",size=2.5) +
labs(fill="Dormancy") +
guides(fill = guide_colourbar(barwidth = 0.5, barheight = 5.5, ticks=F,
label.position = "right", title.position = "top",
label.theme = element_text(angle = 0, size = 8))) +
theme_void()
ggarrange(p1,p2)
# # Add a statistics column for legend (could do this for F1 too ...)
# blocks.m <- reshape2::melt(
# blocks, id.vars = c("id","elevation_mn"),
# measure.vars = c("Greenup_1","MidGreenup_1","Maturity_1","Peak_1",
# "MidGreendown_1","Senescence_1","Dormancy_1"), variable.name = "metric"
# )
# Add a statistics column for legend (could do this for F1 too ...)
blocks.m <- reshape2::melt(
blocks, id.vars = c("id","elevation_mn"),
measure.vars = c("Greenup_1","Maturity_1","Peak_1","Dormancy_1"), variable.name = "metric"
) %>%
mutate(metric = as.character(metric),
metric = as.factor(recode(metric,
"Greenup_1" = "Greenup",
"Maturity_1" = "Maturity",
"Peak_1" = "Peak Greenness",
"Dormancy_1" = "Dormancy")))
blocks.m$metric <- factor(blocks.m$metric, levels=c('Greenup', 'Maturity', 'Peak Greenness', 'Dormancy'))
head(blocks.m)
# Dot plot of phenology metric by elevation for all blocks
(p5 <- ggplot(data=blocks.m, aes(x=value,y=elevation_mn)) +
geom_smooth(method="lm",colour="gray40", fill="gray70", size=0.8) +
geom_point(size=1.2) +
facet_wrap(~metric, scales="free_x") +
theme_light(12) +
labs(x="Date",y="Elevation"))
(p6 <- ggplot(data=blocks.m, aes(x=value,y=elevation_mn,color=metric)) +
geom_point(size=1.2) +
theme_light(12))
ROC, F1, MCC:
accmeas <- read_csv('../../data/tabular/mod/results/accmeas_prop.csv')Rows: 1000 Columns: 18── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
dbl (18): model, prSize, bgSize, cutoff, tp, tpr, fn, tn, tnr, fp, fpr, specificity, precision, recall, f1, accuracy, gmean, mcc
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Calculate the averages for each cutoff value across model runs
accmeas.mn <- accmeas %>%
mutate(cutoff = as.character(cutoff)) %>%
group_by(cutoff) %>%
summarise(
prMn = mean(prSize,na.rm=T), # size of presence data (mean)
prSd = sd(prSize,na.rm=T), # size of presence data (sd)
bgMn = mean(bgSize,na.rm=T), # size of background data (mean)
bgSd = sd(bgSize,na.rm=T), # size of background data (sd)
fprMn = mean(fpr,na.rm=T), # False Positive Rate (mean)
fprSd = sd(fpr,na.rm=T),
tprMn = mean(tpr,na.rm=T), # True Positive Rate (mean)
tprSd = sd(tpr,na.rm=T),
precision = mean(precision,na.rm=T), # precision (mean)
precisionSd = sd(precision,na.rm=T),
recall = mean(recall,na.rm=T), # recall (mean)
recallSd = sd(recall,na.rm=T),
f1 = mean(f1,na.rm=T), # F1 score (mean)
f1Sd = sd(f1,na.rm=T),
gmean = mean(gmean,na.rm=T), # Geometric Mean (mean)
gmeanSd = sd(gmean,na.rm=T),
mcc = mean(mcc,na.rm=T), # Matthew's Correlation Coefficient (mean)
mccSd = sd(mcc,na.rm=T),
accuracy = mean(accuracy,na.rm=T), # Overall accuracy (mean)
accuracySd = sd(accuracy,na.rm=T)
) %>%
ungroup() %>%
mutate(cutoff = round(as.double(cutoff),3))
# Calculate optimum threshold based on F1 statistic
cutoffOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$cutoff
precisionOptF1Mn = accmeas.mn[which.max(accmeas.mn$f1),]$precision
recallOptF1Mn = accmeas.mn[which.max(accmeas.mn$f1),]$recall
fprOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$fprMn
tprOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$tprMn
max_f1 <- max(accmeas.mn$f1, na.rm = TRUE)
# ROC Curve and label the AUC value (approximation)
# Get the AUC approx
# Ensure data is sorted by FPR
accmeas_mn_sorted <- accmeas.mn[order(accmeas.mn$fprMn), ]
# Compute AUC using trapz function in 'pracma'
auc_avg_approx <- trapz(accmeas_mn_sorted$fprMn, accmeas_mn_sorted$tprMn)
fs2a <- ggplot(data=accmeas) +
geom_line(aes(x=fpr,y=tpr,color=factor(model)),linewidth=0.4) +
scale_color_viridis_d(option="turbo") +
geom_line(data=accmeas.mn, aes(x = fprMn, y = tprMn), color = "black", size = 0.8) + # ROC curve average
scale_x_continuous(limits=c(0,1)) + # Set x axis limits from 0 to 1
scale_y_continuous(limits=c(0,1)) +
labs(x='False Positive Rate', y='True Positive Rate', tag="A") +
geom_point(aes(x = fprOptMn, y = tprOptMn),
color = "red", size = 3, shape = 19) + # optimal threshold point
geom_text(aes(x=fprOptMn, y=tprOptMn),
label = paste0('Optimal threshold: ', round(cutoffOptMn,3),'\nApprox. AUC: ', round(auc_avg_approx,3)),
nudge_x = 0.38, nudge_y = -0.08, size = 3.5) +
coord_fixed(ratio = 1) + # to keep the x and y axes scales same
geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "black") + # diagonal
theme_light() +
theme(legend.position = "none",
axis.title.y = element_text(size=11,face="italic",
margin = unit(c(0,0.4,0,0), "cm")),
axis.title.x = element_text(size=11,face="italic",
margin = unit(c(0.4,0,0,0), "cm")),
axis.text = element_text(size=10),
strip.text.x = element_text(size = 11))
fs2b <- ggplot(data=accmeas) +
geom_line(aes(x=cutoff,y=f1,color=factor(model)),linewidth=0.4) +
scale_color_viridis_d(option="turbo") +
geom_line(data=accmeas.mn, aes(x=cutoff, y = f1), color = "black", size = 0.8) + # ROC curve average
# geom_vline(xintercept=0.424, linetype="dashed", color="black") +
geom_point(aes(x=cutoffOptMn, y=max_f1),
color = "red", size = 3, shape = 19) +
geom_text(aes(x=cutoffOptMn, y=max_f1),
label = paste0('Optimal threshold: ', round(cutoffOptMn,3)),
nudge_x = 0.0, nudge_y = 0.08, size = 3.5) +
scale_x_continuous(limits=c(0,1)) + # Set x axis limits from 0 to 1
scale_y_continuous(limits=c(0,1)) +
labs(x='Threshold', y='F1 Score', tag="B") +
coord_fixed(ratio = 1) + # to keep the x and y axes scales same
theme_light() +
theme(legend.position = "none",
axis.title.y = element_text(size=11,face="italic",
margin = unit(c(0,0.4,0,0), "cm")),
axis.title.x = element_text(size=11,face="italic",
margin = unit(c(0.4,0,0,0), "cm")),
axis.text = element_text(size=10),
strip.text.x = element_text(size = 11))
(fs2 <- ggarrange(fs2a,fs2b))
ggsave(fs2, file="../../figures/FigureS2_AUC_F1Max.png", dpi=300)
rm(fs2,fs2a,fs2b,accmeas_mn_sorted,auc_avg_approx)
Table S1: Performance metrics for the optimum cutoff value
Create a pretty table.
library(flextable, quietly = T)
# Get the best F1 score for each model run
accmeas.best <- accmeas %>%
mutate(model = as.factor(model)) %>%
group_by(model) %>%
top_n(1,f1) %>%
ungroup() %>%
mutate(model_iter = row_number()) %>%
select(model,prSize,bgSize,tpr,precision,recall,f1,cutoff)
head(accmeas.best,10)
# Fix names and add units
names(accmeas.best) <- c("Model Seed",
"# of Presence",
"# of Background",
"True Positive Rate",
"Precision",
"Recall",
"F1-Score",
"Threshold")
# Set font name for table
fontname <- "Times New Roman"
# Create the flextable
(ft1 <- flextable(accmeas.best) %>%
font(fontname = fontname, part = "all") %>%
autofit() %>% fit_to_width(6.5))Model Seed | # of Presence | # of Background | True Positive Rate | Precision | Recall | F1-Score | Threshold |
|---|---|---|---|---|---|---|---|
847 | 2,901 | 25,590 | 0.8700448 | 0.9121793 | 0.8700448 | 0.8906140 | 0.3939394 |
991 | 6,270 | 33,755 | 0.8848485 | 0.9202189 | 0.8848485 | 0.9021872 | 0.2727273 |
343 | 7,868 | 31,484 | 0.9246314 | 0.9192570 | 0.9246314 | 0.9219364 | 0.4242424 |
197 | 8,184 | 38,450 | 0.8925953 | 0.9248006 | 0.8925953 | 0.9084126 | 0.3434343 |
783 | 6,175 | 29,167 | 0.9206478 | 0.9243902 | 0.9206478 | 0.9225152 | 0.5353535 |
9 | 4,467 | 17,982 | 0.9131408 | 0.9575117 | 0.9131408 | 0.9348000 | 0.6565657 |
283 | 1,792 | 18,536 | 0.8683036 | 0.9152941 | 0.8683036 | 0.8911798 | 0.5353535 |
777 | 5,743 | 24,471 | 0.9254745 | 0.9311493 | 0.9254745 | 0.9283032 | 0.3131313 |
588 | 4,441 | 25,921 | 0.9119568 | 0.9414226 | 0.9119568 | 0.9264555 | 0.5050505 |
708 | 5,958 | 31,742 | 0.9026519 | 0.9380778 | 0.9026519 | 0.9200240 | 0.4343434 |
print(ft1, preview = "docx")
# Write to a CSV
write_csv(accmeas.best, "../../figures/TableS1_Accuracy_MaxF1.csv")
Clean up!
---
title: "Figures"
output: html_notebook
---

```{r include=F}
knitr::opts_knit$set(root.dir = '~/Library/CloudStorage/OneDrive-Personal/mcook/aspen-fire')
source('setup.R')
```

## STUDY AREA

```{r}
ggplot() +
  geom_sf(data=srme) +
  geom_sf(data=wrnf, fill="darkgrey") +
  coord_sf(crs="EPSG:32613") +
  theme_bw(11)
```

## Figure 1: Surface Reflectance / Spectral Response

```{r}
head(ts)
```

Tidy the time-series data.

1. Remove cloud-contaminated pixels,
2. Pivot the table to gather bands,
3. Generate mean/median/stdev of reflectance by image date,
4. Calculate weekly summaries

Filter using the pixel cloud probability, scene classification, and remove erroneously high reflectance values:

```{r fig.width=5, fig.height=3.5}

tsp <- ts %>%
  filter(
    probability < 51, # less than 51% cloud probability
    ! SCL > 7 & SCL <= 10, # remove points labeled as cloud
    B8 < 5000 # remove points with very high near-infrared reflectance
  ) %>%
  # add the month as a column
  mutate(month = month(image_date, label=TRUE, abbr=TRUE))

tsp %>%
  group_by(image_date) %>%
  summarize(band = median(B8)) %>%
  ggplot(aes(x=image_date,y=band)) +
  geom_point(size=1.2) +
  geom_smooth(method="gam") +
  theme_bw(14)

ggplot(data=tsp, aes(x=B8, y=month)) +
  geom_boxplot() +
  coord_flip() +
  theme_bw()

rm(ts)

```

Now pivot the table longer:

```{r}

tsp <- tsp %>%
  select(c(starts_with("B"),image_date,
           SLAVI,NDRE,NDMI,ChlRE,IRECI)) %>%
  group_by(image_date) %>%
  summarize_all(list(median)) %>%
  pivot_longer(
    cols = -image_date,
    names_to="band",
    values_to="refl"
  ) %>%
  # add a month and week label
  mutate(
    month = month(image_date, label=TRUE, abbr=TRUE),
    week = isoweek(image_date),
    week2 = cut.Date(image_date, breaks="2 week", labels=FALSE)
  )

head(tsp)

```

Plot of median daily observations:

```{r fig.height=5, fig.width=9}
# Reorder band factors
tsp$band <- factor(tsp$band, 
                   levels = c("B2","B3","B4","B5","B6",
                              "B7","B8","B8A","B11","B12",
                              "ChlRE","IRECI","NDMI","NDRE","SLAVI"))

tsp %>%
  filter(str_detect(band,"B")) %>%
  ggplot(aes(x=image_date,y=refl,color=factor(band))) +
  geom_line(linewidth=0.4) +
  scale_color_viridis_d(option="turbo") +
  labs(x="Acquisition Date",y="Reflectance",color="S2 MSI Band") +
  theme_bw(14) +
  theme(axis.title.y = element_text(size=11,face="italic",
                                    margin = unit(c(0,0.4,0,0), "cm")),
        axis.title.x = element_text(size=11,face="italic",
                                    margin = unit(c(0.4,0,0,0), "cm")),
        axis.text = element_text(size=10),
        legend.text = element_text(size=10),
        legend.title = element_text(size=11))
```

# Figure 3: Spectral Response of Aspen Forest Presence Data

Gather the seasonal composite start and end dates:

```{r}
# Summer
(start_summer <- week(as.Date("2019-06-01")))
(end_summer <- week(as.Date("2019-09-14")))
# Autumn
(start_autumn <- week(as.Date("2019-09-16")))
(end_autumn <- week(as.Date("2019-11-30")))
```

Generate the weekly median reflectance.

```{r warning=F, message=F, fig.height=5, fig.width=9}

# List for band colors

band_colors <- c(
  "B2"  = "#1E90FF",  # Blue: Dodger Blue
  "B3"  = "#00FF7F",  # Green: Spring Green
  "B4"  = "#FF0000",  # Red: Pure Red
  "B5"  = "#FFA07A",  # Red Edge: Light Salmon
  "B6"  = "#FF8C00",  # NIR: Dark Orange
  "B7"  = "#D2691E",  # NIR: Chocolate
  "B8"  = "#FFD700",  # NIR: Gold
  "B8A" = "#B8860B",  # NIR: Dark Goldenrod
  "B11" = "#008B8B",  # SWIR: Dark Cyan (dark shade of teal)
  "B12" = "#800080"   # SWIR: Purple
)


# Group by week, calculate the mean, plot

f3a <- tsp %>%
  filter(str_detect(band,"B")) %>%
  group_by(band,week) %>%
  summarize(refl = mean(refl)) %>%
  
  ggplot(aes(x=week,y=refl,color=factor(band))) +
  geom_line(linewidth=0.6) +
  scale_color_manual(values = band_colors) +
  labs(x="Acquisition Week",y="Reflectance",color=NULL) +
  theme_bw(11) +
  theme(axis.title.y = element_text(size=11,face="italic",
                                    margin = unit(c(0,0.4,0,0), "cm")),
        axis.title.x = element_text(size=11,face="italic",
                                    margin = unit(c(0.4,0,0,0), "cm")),
        axis.text = element_text(size=10),
        legend.text = element_text(size=8),
        legend.title = element_text(size=8),
        legend.position = c(0.2,0.85),
        legend.direction = "horizontal",
        legend.background = element_rect(fill="white", color="black"),
        plot.margin = unit(c(1.2,0.5,1.2,0.5), 'lines'),
        text = element_text(family = "Arial")) +
  annotate('rect', xmin=start_summer, xmax=end_autumn, ymin=0, ymax=4000, alpha=.3, fill='gray') +
  geom_vline(xintercept=start_autumn, linetype="dotted", lwd=0.8)
f3a
```

Figure 1B: Time-series charts of the spectral indices:

```{r message=F, warning=F}

# All bands

tsp %>%
  group_by(band,week) %>%
  summarize(refl = mean(refl)) %>%
  ggplot(aes(x=week, y=refl, group=1)) +
  geom_line() +
  annotate('rect', xmin=22, xmax=48, ymin=0, ymax=Inf, alpha=.3, fill='gray') +
  geom_vline(xintercept=37, linetype="dotted") +
  facet_wrap(. ~ band, scales = "free") +
  theme_minimal() +
  theme(axis.title.y = element_text(size=8,face="italic",
                                    margin = unit(c(0,0.4,0,0), "cm")),
        axis.title.x = element_text(size=8,face="italic",
                                    margin = unit(c(0.4,0,0,0), "cm")),
        axis.text = element_text(size=7),
        strip.text.x = element_text(size = 8))

# Spectral indices

f3b <- tsp %>%
  filter(!str_detect(band,"B")) %>%
  group_by(band,week) %>%
  summarize(refl = mean(refl)) %>%
  ggplot(aes(x=week, y=refl, group=1)) +
  geom_line(linewidth=0.6) +
  labs(x="Acquisition Week",y="Reflectance") +
  annotate('rect', xmin=start_summer, xmax=end_autumn, ymin=0, ymax=Inf, alpha=.3, fill='gray') +
  geom_vline(xintercept=start_autumn, linetype="dotted", lwd=0.8) +
  facet_wrap(. ~ band, scales = "free") +
  theme_light(11) +
  theme(axis.title.y = element_text(size=11,face="italic",
                                    margin = unit(c(0,0.4,0,0), "cm")),
        axis.title.x = element_text(size=11,face="italic",
                                    margin = unit(c(0.4,0,0,0), "cm")),
        axis.text = element_text(size=10),
        strip.text.x = element_text(size = 11),
        plot.margin = unit(c(1.2,0.5,1.2,0.5), 'lines'),
        text = element_text(family = "Arial"))
f3b

```

Figure 3C: Boxplot of seasonal composites:

```{r}

# Reorder band factors
tsp$band <- factor(tsp$band, 
                   levels = c("B2","B3","B4","B5","B6",
                              "B7","B8","B8A","B11","B12",
                              "ChlRE","IRECI","NDMI","NDRE","SLAVI"))

cols <- c(
  "Summer" = "#87CEEB", 
  "Autumn" = "#DAA520"
)

seasonal <- tsp %>%
  mutate(season = if_else(week>=22&week<=37,"Summer","na"),
         season = if_else(week>=37&week<=48,"Autumn",season)) %>%
  filter(season!="na")

seasonal$season <- factor(seasonal$season, levels = c("Summer","Autumn"))
  
f3c <- seasonal %>%
  filter(str_detect(band,"B")) %>%
  ggplot(aes(x=band,y=refl,fill=season)) +
  geom_boxplot(outlier.size = 0.5, outlier.color = "grey30") +
  scale_fill_manual(values = cols) +
  theme_bw(16) +
  labs(x="Sentinel-2 MSI Band",y="Reflectance") +
  theme(axis.title.y = element_text(size=11,face="italic",
                                    margin = unit(c(0,0.4,0,0), "cm")),
        axis.title.x = element_text(size=11,face="italic",
                                    margin = unit(c(0.4,0,0,0), "cm")),
        axis.text = element_text(size=10),
        legend.position = "none",
        plot.margin = unit(c(1.2,0.5,1.2,0.5), 'lines'),
        text = element_text(family = "Arial"))
f3c

```

Figure 1D: Seasonal differences in the spectral indices

```{r}

f3d <- seasonal %>%
  filter(!str_detect(band,"B")) %>%
  ggplot(aes(y=refl)) +
  geom_boxplot(aes(fill=season)) +
  scale_fill_manual(values = cols) +
  facet_wrap(. ~ band, scales = "free") +
  theme_bw(14) +
  labs(fill="Season",y="Value") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.title.y = element_text(size=11,face="italic",
                                    margin = unit(c(0,0.4,0,0), "cm")),
        axis.title.x = element_text(size=11,face="italic",
                                    margin = unit(c(0.4,0,0,0), "cm")),
        axis.text = element_text(size=10),
        strip.text.x = element_text(size = 11),
        legend.position = c(0.85,0.2),
        legend.title = element_text(size=16),
        legend.text = element_text(size=15),
        plot.margin = unit(c(1.2,0.5,1.2,0.5), 'lines'),
        text = element_text(family = "Arial"))
f3d

```

Combine the plots:

```{r fig.width=14, fig.height=8, message=F}

# Arrange in a multi-panel plot

f3 <- ggarrange(f3a,f3b,f3c,f3d, nrow=2,ncol=2,widths=c(1, 0.75), labels = c("A", "B", "C", "D"))
f3

# Save it out

ggsave(f3, file = "../../figures/Figure3_SpectralResponse.png",
       dpi = 300, bg="white") # adjust dpi accordingly

```

Version 2:

```{r fig.width=14, fig.height=8}

f3_ <- ggarrange(f3a,ggarrange(f3c,f3d,nrow=1,ncol=2),
                  ncol=1,widths=c(1, 0.75),
                  labels = c("A", "B", "C"))
f3_

ggsave(f3_, file = "../../figures/Figure3_SpectralResponse_v2.png",
       dpi = 300, bg="white") # adjust dpi accordingly

```

Clean up the time-series / spectral response data:

```{r}
rm(f3,f3_,f3a,f3b,f3c,f3d,seasonal,tsp)
gc()
```

## Figure 4: Model accuracy and feature importance plot

Best Performing Model:


Plot the AUC-PR and AUC-ROC.

```{r}
glimpse(accmeas)
```

Calculate the model averages using the optimum cutoff value:

```{r}
# Without taking the average

print("~~~~~Total (not averageing)~~~~~")

(cutoffOptF1 = accmeas[which.max(accmeas$f1),]$cutoff)
(precisionOptF1 = accmeas[which.max(accmeas$f1),]$precision)
(recallOptF1 = accmeas[which.max(accmeas$f1),]$recall)
(fprOpt = accmeas[which.max(accmeas$f1),]$fpr)
(tprOpt = accmeas[which.max(accmeas$f1),]$tpr)

# With the aggregation/averaging across models and threshold values

# Calculate the averages for each cutoff value across model runs

accmeas.mn <- accmeas %>%
  mutate(cutoff = as.character(cutoff)) %>%
  group_by(cutoff) %>%
  summarise(
    prMn = mean(prSize,na.rm=T), # size of presence data (mean)
    prSd = sd(prSize,na.rm=T), # size of presence data (sd)
    bgMn = mean(bgSize,na.rm=T), # size of background data (mean)
    bgSd = sd(bgSize,na.rm=T), # size of background data (sd)
    fprMn = mean(fpr,na.rm=T), # False Positive Rate (mean)
    fprSd = sd(fpr,na.rm=T),
    tprMn = mean(tpr,na.rm=T), # True Positive Rate (mean)
    tprSd = sd(tpr,na.rm=T),
    precision = mean(precision,na.rm=T), # precision (mean)
    precisionSd = sd(precision,na.rm=T), 
    recall = mean(recall,na.rm=T), # recall (mean)
    recallSd = sd(recall,na.rm=T),
    f1 = mean(f1,na.rm=T), # F1 score (mean)
    f1Sd = sd(f1,na.rm=T),
    gmean = mean(gmean,na.rm=T), # Geometric Mean (mean)
    gmeanSd = sd(gmean,na.rm=T),
    mcc = mean(mcc,na.rm=T), # Matthew's Correlation Coefficient (mean)
    mccSd = sd(mcc,na.rm=T),
    accuracy = mean(accuracy,na.rm=T), # Overall accuracy (mean)
    accuracySd = sd(accuracy,na.rm=T)
  ) %>%
  ungroup() %>%
  mutate(cutoff = round(as.double(cutoff),3))

# Calculate optimum threshold based on F1 statistic

cutoffOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$cutoff
precisionOptF1Mn = accmeas.mn[which.max(accmeas.mn$f1),]$precision
recallOptF1Mn = accmeas.mn[which.max(accmeas.mn$f1),]$recall
fprOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$fprMn
tprOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$tprMn
max_f1 <- max(accmeas.mn$f1, na.rm = TRUE)

```

```{r}
# F1 based
print("F1-based accuracy metrics:")
paste0("F1 Score: ",(f1Opt = accmeas.mn[which.max(accmeas.mn$f1),]$f1))
paste0("Overall Accuracy: ",(oaOpt = accmeas.mn[which.max(accmeas.mn$f1),]$accuracy))
paste0("Precision: ",(precOpt = accmeas.mn[which.max(accmeas.mn$f1),]$precision))
paste0("Recall: ",(recOpt = accmeas.mn[which.max(accmeas.mn$f1),]$recall))
paste0("Cutoff: ",(threshOpt = accmeas.mn[which.max(accmeas.mn$f1),]$cutoff))

# Mathews Correlation Coefficient
print("---------------------------")
print("MCC-based accuracy metrics:")
paste0("MCC: ",(mccOpt = accmeas.mn[which.max(accmeas.mn$mcc),]$mcc))
paste0("Overall Accuracy: ",(oaOptmcc = accmeas.mn[which.max(accmeas.mn$mcc),]$accuracy))
paste0("Precision: ",(precOptmcc = accmeas.mn[which.max(accmeas.mn$mcc),]$precision))
paste0("Recall: ",(recOptmcc = accmeas.mn[which.max(accmeas.mn$mcc),]$recall))
paste0("Cutoff: ",(threshOptmcc = accmeas.mn[which.max(accmeas.mn$mcc),]$cutoff))
```

Plot the model accuracy results for the optimum threshold.

```{r}

# Get the best F1 score for each model run
accmeas.best <- accmeas %>% 
  group_by(factor(model)) %>% top_n(1,f1) %>%
  ungroup() %>%
  mutate(model_iter = row_number())

print(paste0("Cutoff: Mean = ",round(mean(accmeas.best$cutoff),3),"; Standard Deviation: ",round(sd(accmeas.best$cutoff),3)))
print(paste0("F1 Score: Mean = ",round(mean(accmeas.best$f1),3),"; Standard Deviation: ",round(sd(accmeas.best$f1),3)))

```

Figure 4A: Model Accuracy (F1 and OA)

```{r message=F, warning=F}

cols <- c("F1 Score"="#1f78b4","Overall Accuracy"="gray75")

f4a <- ggplot(data=accmeas.best) +
  geom_hline(yintercept=0.972760973096824,linetype="dashed",color="gray85") +
  geom_hline(yintercept=0.910339477355629,linetype="dashed",color="#1f78b4") +
  geom_line(aes(x=factor(model_iter),y=accuracy,group=1),color="gray65") +
  geom_line(aes(x=factor(model_iter),y=f1,group=1),color="#1f78b4") +
  geom_point(aes(x=factor(model_iter),y=f1,size=f1), color="#1f78b4") +
  geom_point(aes(x=factor(model_iter),y=accuracy,size=accuracy), color="gray65") +
  scale_size(range = c(3,6)) +
  coord_cartesian(ylim = c(0.85, 1)) +
  labs(x="Model Iteration", y="Score", tag="A") +
  annotate("text", x = 3, y = 0.99, label = paste("Average Overall Accuracy: ", round(oaOpt, 2))) +
  annotate("text", x = 2.6, y = 0.88, label = paste("Average F1 Score: ", round(f1Opt, 2))) +
  theme_bw() +
  guides(size="none") +
  theme(
        plot.margin = unit(c(0.2,0.2,0.2,0.2), 'lines'),
        axis.title.y = element_text(size=11,face="italic",
                                    margin = unit(c(0,0.4,0,0), "cm")),
        axis.title.x = element_text(size=11,face="italic",
                                    margin = unit(c(0.4,0,0,0), "cm")),
        axis.text = element_text(size=10),
        strip.text.x = element_text(size = 11),
        text = element_text(family = "Arial"))
f4a

ggsave(f4a, file = "../../figures/Figure4A_Best_Model_Acc_F1.png",
       dpi = 300, bg="white") # adjust dpi accordingly

```

Figure 4B: Sensitivity Analysis

```{r}

# Labels for each model subset

labels_ = c(
  "winter_sar" = "M1", 
  "summer_sar" = "M2",
  "summer_winter_sar_text" = "M3",
  "summer_winter_sar" = "M4",
  "autumn_spectral" = "M5",
  "summer_spectral" = "M6",
  "summer_spectral_ind" = "M7",
  "spectral_sar_ind" = "M8",
  "summer_autumn_spectral" = "M9",
  "spectral_sar" = "M10",
  "final" = "Final Model"
)

# Add in the final model to the sensitivity analysis

add <- accmeas.best %>%
  ungroup() %>%
  select(model,f1) %>%
  mutate(subset = "final") %>%
  rename(f1Max = f1)
glimpse(add)
accmeas.s <- accmeas.s %>%
  bind_rows(add)
rm(add)

print(unique(accmeas.s$subset))

# Generate the box plots of F1 score

f4b <- ggplot(data=accmeas.s, aes(x=reorder(subset,f1Max),y=f1Max)) +
  geom_boxplot() +
  scale_x_discrete(labels=labels_) +
  labs(x="Input Features",y="Maximum F1 Score", tag="B") +
  theme_bw(11) +
  theme(
        plot.margin = unit(c(0.2,0.2,0.2,0.2), 'lines'),
        axis.title.y = element_text(size=11,face="italic",
                                    margin = unit(c(0,0.4,0,0), "cm")),
        axis.title.x = element_text(size=11,face="italic",
                                    margin = unit(c(0.4,0,0,0), "cm")),
        axis.text = element_text(size=10),
        strip.text.x = element_text(size = 11),
        axis.text.x = element_text(angle = 45, hjust=1),
        text = element_text(family = "Arial"))
f4b

# Save out

ggsave(f4b, file = "../../figures/Figure4B_Model_Sensitivity.png",
       dpi = 300, bg="white") # adjust dpi accordingly
```

Combine the two figures for Figure 4AB:

```{r fig.width=7.5, fig.height=6}

f4 <- ggarrange(f4a,f4b,nrow=2)
f4

ggsave(f4, file = "../../figures/Figure4AB_Model_Accuracy_Sensitivity.png",
       width=7.5, height=7.5, dpi = 300, bg="white") # adjust dpi accordingly

```

## Figure 6: Feature importance from the best model

```{r}
glimpse(ftr_imp)
```

```{r message=F, warning=F}

# # Tidy the data frame
# df.imp <- ftr_imp %>% 
#   select(-c(.geo,`system:index`)) %>%
#   rename(model = seed) %>%
#   mutate(model = as.factor(model)) %>%
#   # Summarize across to grab the relative importance
#   rowwise() %>%
#   mutate(total = sum(c_across(B11_autumn:VV_winter_ent))) %>%
#   ungroup() %>%
#   mutate(across(B11_autumn:VV_winter_ent, ~ . * 100 / total)) %>%
#   select(-total)
# glimpse(df.imp)

# Tidy the data frame
df.imp <- ftr_imp %>% 
  select(-c(.geo,`system:index`)) %>%
  rename(model = seed) %>%
  mutate(model = as.factor(model)) %>%
  # Summarize across to grab the relative importance
  group_by(model) %>%
  mutate(total = sum(across(B11_autumn:VV_winter_ent))) %>%
  mutate(across(B11_autumn:VV_winter_ent, ~ . / total)) %>%
  ungroup() %>%
  select(-total)

```

```{r}

# Pivot longer
df.imp.p <- df.imp %>%
  pivot_longer(cols = -model) %>%
  rename(importance = value,
         band = name) %>%
  mutate(season = if_else(str_detect(band,"_autumn"), "Autumn Spectral", "Summer Spectral"),
         season = if_else(str_detect(band,"VV_winter"), "Winter SAR", season),
         season = if_else(str_detect(band,"VH_winter"), "Winter SAR", season),
         season = if_else(str_detect(band,"VV_summer"), "Summer SAR", season),
         season = if_else(str_detect(band,"VH_summer"), "Summer SAR", season))
glimpse(df.imp.p)

```

```{r message=F}

# Grab the top 20 over all model runs

top <- df.imp.p %>%
  group_by(band) %>%
  summarize(median = median(importance)) %>%
  ungroup()

top <- head(arrange(top,desc(median)), n = 20)

# Color palette

cols <- c(
  "Summer Spectral" = "#87CEEB", 
  "Autumn Spectral" = "#DAA520",
  "Winter SAR" = "gray29",
  "Summer SAR" = "gray89"
)

# Boxplot

# %>% filter(band %in% top$band)
f6 <- ggplot(data=df.imp.p %>% filter(band %in% top$band), 
              aes(x=reorder(band,importance), fill=season)) +
  geom_boxplot(aes(y=importance), position = position_dodge(0.5)) +
  scale_fill_manual(values = cols, 
                    labels = c("Autumn Spectral","Summer SAR","Summer Spectral","Winter SAR")) +
  coord_flip() +
  theme_bw(11) +
  theme(axis.text.x = element_text(size=11),
        axis.text.y = element_text(angle = 0, vjust=0, size=11),
        axis.title = element_text(size=11,face="italic"),
        legend.position = "bottom",
        legend.justification = c(1.2,0.5), # Left-aligns the legends
        legend.text = element_text(size=11),
        text = element_text(family = "Arial"),
        plot.margin=unit(c(0.5,0.5,0.5,0.5),"cm")) +
  labs(x="Feature", y="Importance", fill="Season: ")
f6

ggsave(f6, file = "../../figures/Figure6_FeatureImportance.png",
       dpi=300, bg="white") # adjust dpi accordingly

```

Clean up!

```{r}
rm(list = ls.str(mode = 'numeric'))
rm(accmeas,accmeas.best,accmeas.mn,accmeas.s,df.imp,
   df.imp.p,f4,f4a,f4b,f6,ftr_imp,top)
gc()
```

## Figures 7, 8 - Case Study: Landscape Patch Dynamics

# Figure 7: Spatial Agreement

```{r}

brewer.pal(n=5,"Set1")

cols <- c("#4DAF4A", "#984EA3", "#FF7F00")

glimpse(ref.global)

# Get an average f1 score table
f1mn <- ref.global %>%
  group_by

# Add a statistics column for legend (could do this for F1 too ...)
ref.global.m <- reshape2::melt(
  ref.global, id.vars = c("blocksize", "source", "region"), 
  measure.vars = c("prec", "rec"), variable.name = "statistic"
)

glimpse(ref.global.m)

f7a <- ggplot(data=ref.global.m) +
  geom_line(aes(x=blocksize,y=value,color=factor(source), linetype=statistic), linewidth=0.6) +
  geom_point(data=ref.global, aes(x=blocksize, y=f1, color=source)) +
  scale_x_continuous(breaks=c(1,3,5,7,9)) +
  scale_linetype_manual(values = c("prec" = "dotted", "rec" = "solid"), name="Statistic: ",
                        labels=c("Precision","Recall")) +
  facet_wrap(~region, scales = "fixed") +
  scale_color_manual(values=cols, labels=c("LANDFIRE EVT","USFS TreeMap","USFS ITSP"), name="Source: ") +
  theme_light(14) +
  ylim(0,1) +
  theme(legend.position = "top",
        legend.box = "vertical",
        legend.justification = c(0.5,0.5), # Left-aligns the legends
        legend.spacing.y = unit(-10, "pt"), # Adjusts the gap between the legends
        legend.text = ggtext::element_markdown(halign = 0), # This aligns each individual legend's text to the left
        axis.title = element_text(size=12,face="italic"),
        text = element_text(family = "Arial")) +
  labs(x="Blocksize (pixels)",y="Value")
  
f7a

ggsave(f7a, file = "../../figures/Figure7_Global_PrecRec_Agreement.png", dpi=300)

```

Focal accuracy:

```{r message=F}

# ref.focal$refbudens <- log(1 + ref.focal$tp + ref.focal$fn)
# 
# ggplot(ref.focal %>% filter(region=="SRME"), aes(x = prec, y = rec, color = refbudens)) +
#    geom_point(size = 2, alpha = 0.9) +
#    scale_color_viridis_c() +
#    facet_grid(rows = vars(geog_scale), cols = vars(blocksize), scales = "fixed") +
#    theme_light() +
#    labs(
#       title = 'Spatially explicit accuracy: Precision (x) vs. Recall (y)',
#       subtitle = 'for multiple analytical units and spatial support levels',
#       color = 'refbudens',
#       x = 'Precision',
#       y = 'Recall'
#    ) +
#    coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) +
#    theme(legend.position = "bottom")

```

Join the two plots:

```{r}
# f7 <- ggarrange(global_precrec,focal.prec,focal.rec,nrow=1,labels=c("A","B","C"))
# f7
# ggsave(f7, file="../../figures/Figure7_Global_Focal_Agreement.png", dpi=300)
```

Clean up! 

```{r}
rm(ref.global,ref.global.m,f7a,f7b,f7,ref.focal)
gc()
```

# Figure 8: Landscape Patch Dynamics

```{r}
unique(factor(patch_metrics$source))
summary(patch_metrics)
```

Calculate some grouped statistics (to compare with the class metrics results)

```{r message=F, warning=F, fig.height=8, fig.width=8}

# Also create a grouped summary
(patch.summary <- patch_metrics %>%
  group_by(source,region) %>%
  summarize(area_md = median(area), # convert to hectares
            area_mn = mean(area),
            area_sum = sum(area),
            perim_md = median(perimeter),
            perim_mn = mean(perimeter),
            par_md = median(perimeter_area_ratio),
            par_mn = mean(perimeter_area_ratio),
            si_md = median(shape_index),
            si_mn = mean(shape_index)) %>%
  ungroup())

(patch.long <- patch.summary %>%
  pivot_longer(cols = c(contains("_")),
               names_to = "metric",
               values_to = "value"))

patch.long$source <- factor(patch.long$source,levels=c('Aspen10m', 'Aspen30m', 'LFEVT', 'TreeMap', 'ITSP'))

(ggplot(data = patch.long, aes(x=source, y=value, fill=region)) +
    geom_bar(stat="identity", position="dodge", width=0.7) +
    facet_wrap(~metric, scales="free") + 
    theme_minimal(14) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1),
    text = element_text(family = "Arial")))

(f8s <- ggplot(data = patch.long, aes(x=source, y=value, fill=region)) +
    geom_bar(stat="identity", position="dodge", width=0.7) + 
    theme_minimal(14) +
    theme(axis.text.x = element_text(angle = 45, hjust = 1),
    text = element_text(family = "Arial")))

```

Grab some statistics on the patch size:

```{r}
(a <- patch.long %>% filter(metric=="area_mn",source=="Aspen10m"))
(b <- patch.long %>% filter(metric=="area_mn",source=="LFEVT"))

(c <- patch.long %>% filter(metric=="area_md",source=="Aspen10m"))
(d <- patch.long %>% filter(metric=="area_md",source=="LFEVT"))

print("Mean and median patch sizes: ")
paste0("Aspen10m mean: ",a$value)
paste0("LFEVT mean: ",b$value)
paste0("Aspen10m median: ",c$value)
paste0("LFEVT mean: ",d$value)

print("Difference in mean area: ")
((b$value-a$value)/b$value)*100

print("Difference in median area: ")
((d$value-c$value)/d$value)*100

print("Mean Perimeter area ratio: ")
(a <- patch.long %>% filter(metric=="par_mn",source=="Aspen10m"))
(b <- patch.long %>% filter(metric=="par_mn",source=="LFEVT"))

print("Difference in PAR: ")
((b$value-a$value)/b$value)*100

rm(a,b,c,d)
```

Boxplot as facet wrap for patch metrics:

```{r fig.height=5, fig.width=10}

brewer.pal(n=5,"Set1")

cols <- c("#005a32","#a1d99b")

(df <- patch_metrics %>%
  # mutate(area_ha = area*100) %>%
  select(-c(X,index,shape_index)) %>%
  pivot_longer(contains(c("area","perimeter_area_ratio"))) %>%
  arrange(source, desc(value)) %>%
  mutate(name = as.character(name),
         name = recode(name,
                       "area" = "Patch Size (ha)",
                       "perimeter_area_ratio" = "Perimeter/Area Ratio")))

df$source <- factor(df$source,
                    levels=c('Aspen10m', 'Aspen30m', 'LFEVT', 'TreeMap', 'ITSP'))
 
(f8a <- ggplot(data=df, aes(y = value, x = factor(source), fill = factor(region))) +
  geom_boxplot(outlier.size = 0.2) +
  scale_y_continuous(trans="log10",labels=label_number_si(accuracy = 1)) +
  scale_fill_manual(values=cols) +
  facet_wrap(~name, scales = "free_y", nrow=1) +
  labs(x="\nData Source",y="Value",title="Patch Metrics", fill="Region: ") +
  theme_light(12) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size=11),
        axis.text.y = element_text(angle = 0, vjust=0.1, size=11),
        axis.title.y = element_text(size=12, face="italic", vjust=1),
        axis.title.x = element_text(size=12, face="italic", vjust=-1),
        plot.margin = unit(c(0.5,0.5,0.5,0.5),"cm"),
        legend.position="top",
        legend.spacing.x = unit(0.5,"cm"),
        text = element_text(family = "Arial")))

ggsave(f8a, file = "../../figures/Figure8A_patch_metrics.png", dpi = 300) # adjust dpi accordingly

```

Plot class metrics as facet wrap:

```{r fig.height=5, fig.width=10, message=F}

# Calculate area in square meters
srme_area_km2 <- st_area(srme) / 1e6
wrnf_area_km2 <- st_area(wrnf) / 1e6

(df <- class_metrics %>%
  mutate(total_area = total_area*0.01,
         prop_area = if_else(region=="SRME", as.double((total_area/srme_area_km2*100)), 0.0),
         prop_area = if_else(region=="WRNF", as.double((total_area/wrnf_area_km2*100)), prop_area)) %>%
  select(-c(X)) %>%
  pivot_longer(cols = c(contains("_")),
               names_to = "metric",
               values_to = "value") %>%
  mutate(metric = as.character(metric),
         metric = recode(metric,
                         "n_patch" = "Number of Patches",
                         "patch_den" = "Patch Density",
                         "total_area" = "Total area (km2)",
                         "prop_area" = "Proportion of Area")))

df$source <- factor(df$source, levels=c('Aspen10m', 'Aspen30m', 'LFEVT', 'TreeMap', 'ITSP'))
head(df)

(f8b <- ggplot(data=df, aes(y=value, x=factor(source), fill=factor(region))) +
  geom_bar(stat="identity", position="dodge", width=0.7) +
  scale_y_continuous(labels=scales::label_number_si(accuracy = 1)) +
  scale_fill_manual(values=cols) +
  labs(x="", y="Value", title="Landscape Metrics", fill="Region: ") +
  theme(axis.title.x = NULL) +
  facet_wrap(~metric, scales = "free_y") +
  theme_light(12) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size=11),
        axis.text.y = element_text(angle = 0, vjust=0.1, size=11),
        axis.title.y = element_text(size=12, face="italic", vjust=0.5),
        axis.title.x = element_text(size=12, face="italic", vjust=-0.5),
        plot.margin = unit(c(0.5,0.5,0.5,0.5),"cm"),
        legend.position = "top",
        legend.spacing.x = unit(0.5,"cm"),
        text = element_text(family = "Arial")))

ggsave(f8b, file = "../../figures/Figure8B_landscape_metrics.png", dpi = 300) # adjust dpi accordingly

```

```{r fig.width=10, fig.height=10}
(arr <- ggarrange(f8b,f8a,nrow=2,ncol=1,labels=c("A","B"), common.legend = T))
ggsave(arr, file="../../figures/Figure8_Landscape_Patch_Metrics.png", dpi=300)
```

The 10-m aspen classification identified Xx more patch than reference images:

```{r}

print("% Difference in number of patches: ")
a <- df %>% filter(metric=="Number of Patches",source=="Aspen10m")
b <- df %>% filter(metric=="Number of Patches",source=="LFEVT")
((b$value-a$value)/b$value)*100

print("% Difference in patch density: ")
a <- df %>% filter(metric=="Patch Density",source=="Aspen10m")
b <- df %>% filter(metric=="Patch Density",source=="LFEVT")
((b$value-a$value)/b$value)*100

print("% Difference in total area:")
a <- df %>% filter(metric=="Total area (km2)",source=="Aspen10m")
aa <- df %>% filter(metric=="Total area (km2)",source=="Aspen30m")
b <- df %>% filter(metric=="Total area (km2)",source=="LFEVT")
((b$value-a$value)/b$value)*100
((b$value-aa$value)/b$value)*100

rm(a,aa,b)

```


# Supplemental

Figure S1: Phenology across the SRME study region

```{r}

# Spatial map(s) of key phenological metrics

breaks_dates <- as.Date(c("2020-01-01", "2021-01-01", "2022-01-01"))
breaks_numeric <- as.numeric(breaks_dates)  # Convert to numeric (days since 1970-01-01, by default)
labels_dates <- format(breaks_dates, "%Y-%m-%d")

p1 <- ggplot(data=blocks) +
  geom_sf(aes(fill=Maturity_1)) +
  scale_fill_continuous(low = "lightgreen", high = "darkgreen",
                        labels = function(x) format(as.Date(as.numeric(x), origin="1970-01-01"), "%B %d")) +
  geom_sf(data=srme,fill=NA,color="black",size=2.5) +
  labs(fill="Maturity") +
  guides(fill = guide_colourbar(barwidth = 0.5, barheight = 5.5, ticks=F,
                                 label.position = "right", title.position = "top",
                                 label.theme = element_text(angle = 0, size = 8))) +
  theme_void()

p2 <- ggplot(data=blocks) +
  geom_sf(aes(fill=Dormancy_1)) +
  scale_fill_continuous(low = "#ffffd4", high = "#993404",
                        labels = function(x) format(as.Date(as.numeric(x), origin="1970-01-01"), "%B %d")) +
  geom_sf(data=srme,fill=NA,color="black",size=2.5) +
  labs(fill="Dormancy") +
  guides(fill = guide_colourbar(barwidth = 0.5, barheight = 5.5, ticks=F,
                                 label.position = "right", title.position = "top",
                                 label.theme = element_text(angle = 0, size = 8))) +
  theme_void()

ggarrange(p1,p2)

# # Add a statistics column for legend (could do this for F1 too ...)
# blocks.m <- reshape2::melt(
#   blocks, id.vars = c("id","elevation_mn"), 
#   measure.vars = c("Greenup_1","MidGreenup_1","Maturity_1","Peak_1",
#                    "MidGreendown_1","Senescence_1","Dormancy_1"), variable.name = "metric"
# )

# Add a statistics column for legend (could do this for F1 too ...)
blocks.m <- reshape2::melt(
  blocks, id.vars = c("id","elevation_mn"), 
  measure.vars = c("Greenup_1","Maturity_1","Peak_1","Dormancy_1"), variable.name = "metric"
) %>%
  mutate(metric = as.character(metric),
         metric = as.factor(recode(metric,
                         "Greenup_1" = "Greenup",
                         "Maturity_1" = "Maturity",
                         "Peak_1" = "Peak Greenness",
                         "Dormancy_1" = "Dormancy")))
  
blocks.m$metric <- factor(blocks.m$metric, levels=c('Greenup', 'Maturity', 'Peak Greenness', 'Dormancy'))

head(blocks.m)

# Dot plot of phenology metric by elevation for all blocks

(p5 <- ggplot(data=blocks.m, aes(x=value,y=elevation_mn)) +
  geom_smooth(method="lm",colour="gray40", fill="gray70", size=0.8) +
  geom_point(size=1.2) +
  facet_wrap(~metric, scales="free_x") +
  theme_light(12) +
    labs(x="Date",y="Elevation"))

(p6 <- ggplot(data=blocks.m, aes(x=value,y=elevation_mn,color=metric)) +
  geom_point(size=1.2) +
  theme_light(12))

```


ROC, F1, MCC:

```{r}

accmeas <- read_csv('../../data/tabular/mod/results/accmeas_prop.csv')

# Calculate the averages for each cutoff value across model runs

accmeas.mn <- accmeas %>%
  mutate(cutoff = as.character(cutoff)) %>%
  group_by(cutoff) %>%
  summarise(
    prMn = mean(prSize,na.rm=T), # size of presence data (mean)
    prSd = sd(prSize,na.rm=T), # size of presence data (sd)
    bgMn = mean(bgSize,na.rm=T), # size of background data (mean)
    bgSd = sd(bgSize,na.rm=T), # size of background data (sd)
    fprMn = mean(fpr,na.rm=T), # False Positive Rate (mean)
    fprSd = sd(fpr,na.rm=T),
    tprMn = mean(tpr,na.rm=T), # True Positive Rate (mean)
    tprSd = sd(tpr,na.rm=T),
    precision = mean(precision,na.rm=T), # precision (mean)
    precisionSd = sd(precision,na.rm=T), 
    recall = mean(recall,na.rm=T), # recall (mean)
    recallSd = sd(recall,na.rm=T),
    f1 = mean(f1,na.rm=T), # F1 score (mean)
    f1Sd = sd(f1,na.rm=T),
    gmean = mean(gmean,na.rm=T), # Geometric Mean (mean)
    gmeanSd = sd(gmean,na.rm=T),
    mcc = mean(mcc,na.rm=T), # Matthew's Correlation Coefficient (mean)
    mccSd = sd(mcc,na.rm=T),
    accuracy = mean(accuracy,na.rm=T), # Overall accuracy (mean)
    accuracySd = sd(accuracy,na.rm=T)
  ) %>%
  ungroup() %>%
  mutate(cutoff = round(as.double(cutoff),3))

# Calculate optimum threshold based on F1 statistic

cutoffOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$cutoff
precisionOptF1Mn = accmeas.mn[which.max(accmeas.mn$f1),]$precision
recallOptF1Mn = accmeas.mn[which.max(accmeas.mn$f1),]$recall
fprOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$fprMn
tprOptMn = accmeas.mn[which.max(accmeas.mn$f1),]$tprMn
max_f1 <- max(accmeas.mn$f1, na.rm = TRUE)

```


```{r warning=F, message=F}

# ROC Curve and label the AUC value (approximation)

# Get the AUC approx

# Ensure data is sorted by FPR
accmeas_mn_sorted <- accmeas.mn[order(accmeas.mn$fprMn), ]
# Compute AUC using trapz function in 'pracma'
auc_avg_approx <- trapz(accmeas_mn_sorted$fprMn, accmeas_mn_sorted$tprMn)

fs2a <- ggplot(data=accmeas) +
  geom_line(aes(x=fpr,y=tpr,color=factor(model)),linewidth=0.4) +
  scale_color_viridis_d(option="turbo") +
  geom_line(data=accmeas.mn, aes(x = fprMn, y = tprMn), color = "black", size = 0.8) +  # ROC curve average
  scale_x_continuous(limits=c(0,1)) +  # Set x axis limits from 0 to 1
  scale_y_continuous(limits=c(0,1)) +
  labs(x='False Positive Rate', y='True Positive Rate', tag="A") +
  geom_point(aes(x = fprOptMn, y = tprOptMn),
             color = "red", size = 3, shape = 19) +  # optimal threshold point
  geom_text(aes(x=fprOptMn, y=tprOptMn),
            label = paste0('Optimal threshold: ', round(cutoffOptMn,3),'\nApprox. AUC: ', round(auc_avg_approx,3)),
            nudge_x = 0.38, nudge_y = -0.08, size = 3.5) +
  coord_fixed(ratio = 1) +  # to keep the x and y axes scales same
  geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "black") +  # diagonal
  theme_light() +
  theme(legend.position = "none",
        axis.title.y = element_text(size=11,face="italic",
                                    margin = unit(c(0,0.4,0,0), "cm")),
        axis.title.x = element_text(size=11,face="italic",
                                    margin = unit(c(0.4,0,0,0), "cm")),
        axis.text = element_text(size=10),
        strip.text.x = element_text(size = 11))

fs2b <- ggplot(data=accmeas) +
  geom_line(aes(x=cutoff,y=f1,color=factor(model)),linewidth=0.4) +
  scale_color_viridis_d(option="turbo") +
  geom_line(data=accmeas.mn, aes(x=cutoff, y = f1), color = "black", size = 0.8) +  # ROC curve average
  # geom_vline(xintercept=0.424, linetype="dashed", color="black") +
  geom_point(aes(x=cutoffOptMn, y=max_f1),
             color = "red", size = 3, shape = 19) +
  geom_text(aes(x=cutoffOptMn, y=max_f1),
            label = paste0('Optimal threshold: ', round(cutoffOptMn,3)),
            nudge_x = 0.0, nudge_y = 0.08, size = 3.5) +
  scale_x_continuous(limits=c(0,1)) +  # Set x axis limits from 0 to 1
  scale_y_continuous(limits=c(0,1)) +
  labs(x='Threshold', y='F1 Score', tag="B") +
  coord_fixed(ratio = 1) +  # to keep the x and y axes scales same
  theme_light() +
  theme(legend.position = "none",
        axis.title.y = element_text(size=11,face="italic",
                                    margin = unit(c(0,0.4,0,0), "cm")),
        axis.title.x = element_text(size=11,face="italic",
                                    margin = unit(c(0.4,0,0,0), "cm")),
        axis.text = element_text(size=10),
        strip.text.x = element_text(size = 11))

(fs2 <- ggarrange(fs2a,fs2b))

ggsave(fs2, file="../../figures/FigureS2_AUC_F1Max.png", dpi=300)

rm(fs2,fs2a,fs2b,accmeas_mn_sorted,auc_avg_approx)

```

Table S1: Performance metrics for the optimum cutoff value

Create a pretty table.

```{r}

library(flextable, quietly = T)

# Get the best F1 score for each model run
accmeas.best <- accmeas %>% 
  mutate(model = as.factor(model)) %>%
  group_by(model) %>% 
  top_n(1,f1) %>%
  ungroup() %>%
  mutate(model_iter = row_number()) %>%
  select(model,prSize,bgSize,tpr,precision,recall,f1,cutoff)

head(accmeas.best,10)

# Fix names and add units
names(accmeas.best) <- c("Model Seed",
                         "# of Presence",
                         "# of Background",
                         "True Positive Rate",
                         "Precision",
                         "Recall",
                         "F1-Score",
                         "Threshold")

# Set font name for table
fontname <- "Times New Roman"

# Create the flextable
(ft1 <- flextable(accmeas.best) %>%
 font(fontname = fontname, part = "all") %>%
 autofit() %>% fit_to_width(6.5))
print(ft1, preview = "docx")

# Write to a CSV
write_csv(accmeas.best, "../../figures/TableS1_Accuracy_MaxF1.csv")

```

Clean up!

```{r}

```
